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.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+sample_master_planogram.jpeg filter=lfs diff=lfs merge=lfs -text
+tmp.png filter=lfs diff=lfs merge=lfs -text
+tmp/master_tmp.png filter=lfs diff=lfs merge=lfs -text
+tmp/to_score_planogram_tmp.png filter=lfs diff=lfs merge=lfs -text

CONTRIBUTING.md

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+## Contributing to YOLOv5 🚀
+
+We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible, whether it's:
+
+- Reporting a bug
+- Discussing the current state of the code
+- Submitting a fix
+- Proposing a new feature
+- Becoming a maintainer
+
+YOLOv5 works so well due to our combined community effort, and for every small improvement you contribute you will be
+helping push the frontiers of what's possible in AI 😃!
+
+## Submitting a Pull Request (PR) 🛠️
+
+Submitting a PR is easy! This example shows how to submit a PR for updating `requirements.txt` in 4 steps:
+
+### 1. Select File to Update
+
+Select `requirements.txt` to update by clicking on it in GitHub.
+<p align="center"><img width="800" alt="PR_step1" src="https://user-images.githubusercontent.com/26833433/122260847-08be2600-ced4-11eb-828b-8287ace4136c.png"></p>
+
+### 2. Click 'Edit this file'
+
+Button is in top-right corner.
+<p align="center"><img width="800" alt="PR_step2" src="https://user-images.githubusercontent.com/26833433/122260844-06f46280-ced4-11eb-9eec-b8a24be519ca.png"></p>
+
+### 3. Make Changes
+
+Change `matplotlib` version from `3.2.2` to `3.3`.
+<p align="center"><img width="800" alt="PR_step3" src="https://user-images.githubusercontent.com/26833433/122260853-0a87e980-ced4-11eb-9fd2-3650fb6e0842.png"></p>
+
+### 4. Preview Changes and Submit PR
+
+Click on the **Preview changes** tab to verify your updates. At the bottom of the screen select 'Create a **new branch**
+for this commit', assign your branch a descriptive name such as `fix/matplotlib_version` and click the green **Propose
+changes** button. All done, your PR is now submitted to YOLOv5 for review and approval 😃!
+<p align="center"><img width="800" alt="PR_step4" src="https://user-images.githubusercontent.com/26833433/122260856-0b208000-ced4-11eb-8e8e-77b6151cbcc3.png"></p>
+
+### PR recommendations
+
+To allow your work to be integrated as seamlessly as possible, we advise you to:
+
+- ✅ Verify your PR is **up-to-date with origin/master.** If your PR is behind origin/master an
+  automatic [GitHub actions](https://github.com/ultralytics/yolov5/blob/master/.github/workflows/rebase.yml) rebase may
+  be attempted by including the /rebase command in a comment body, or by running the following code, replacing 'feature'
+  with the name of your local branch:
+
+```bash
+git remote add upstream https://github.com/ultralytics/yolov5.git
+git fetch upstream
+git checkout feature  # <----- replace 'feature' with local branch name
+git merge upstream/master
+git push -u origin -f
+```
+
+- ✅ Verify all Continuous Integration (CI) **checks are passing**.
+- ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase
+  but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_  -Bruce Lee
+
+## Submitting a Bug Report 🐛
+
+If you spot a problem with YOLOv5 please submit a Bug Report!
+
+For us to start investigating a possibel problem we need to be able to reproduce it ourselves first. We've created a few
+short guidelines below to help users provide what we need in order to get started.
+
+When asking a question, people will be better able to provide help if you provide **code** that they can easily
+understand and use to **reproduce** the problem. This is referred to by community members as creating
+a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example). Your code that reproduces
+the problem should be:
+
+* ✅ **Minimal** – Use as little code as possible that still produces the same problem
+* ✅ **Complete** – Provide **all** parts someone else needs to reproduce your problem in the question itself
+* ✅ **Reproducible** – Test the code you're about to provide to make sure it reproduces the problem
+
+In addition to the above requirements, for [Ultralytics](https://ultralytics.com/) to provide assistance your code
+should be:
+
+* ✅ **Current** – Verify that your code is up-to-date with current
+  GitHub [master](https://github.com/ultralytics/yolov5/tree/master), and if necessary `git pull` or `git clone` a new
+  copy to ensure your problem has not already been resolved by previous commits.
+* ✅ **Unmodified** – Your problem must be reproducible without any modifications to the codebase in this
+  repository. [Ultralytics](https://ultralytics.com/) does not provide support for custom code ⚠️.
+
+If you believe your problem meets all of the above criteria, please close this issue and raise a new one using the 🐛 **
+Bug Report** [template](https://github.com/ultralytics/yolov5/issues/new/choose) and providing
+a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example) to help us better
+understand and diagnose your problem.
+
+## License
+
+By contributing, you agree that your contributions will be licensed under
+the [GPL-3.0 license](https://choosealicense.com/licenses/gpl-3.0/)

Dockerfile

@@ -0,0 +1,60 @@
+# # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# # Start FROM Nvidia PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
+# FROM nvcr.io/nvidia/pytorch:21.05-py3
+
+# # Install linux packages
+# RUN apt update && apt install -y zip htop screen libgl1-mesa-glx
+
+# # Install python dependencies
+# COPY requirements.txt .
+# RUN python -m pip install --upgrade pip
+# RUN pip uninstall -y nvidia-tensorboard nvidia-tensorboard-plugin-dlprof
+# RUN pip install --no-cache -r requirements.txt coremltools onnx gsutil notebook
+# RUN pip install --no-cache -U torch torchvision numpy
+# # RUN pip install --no-cache torch==1.9.0+cu111 torchvision==0.10.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html
+
+# # Create working directory
+# RUN mkdir -p /usr/src/app
+# WORKDIR /usr/src/app
+
+# # Copy contents
+# COPY . /usr/src/app
+
+# # Set environment variables
+# ENV HOME=/usr/src/app
+
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/yolov5:latest && sudo docker build -t $t . && sudo docker push $t
+
+# Pull and Run
+# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t
+
+# Pull and Run with local directory access
+# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/datasets:/usr/src/datasets $t
+
+# Kill all
+# sudo docker kill $(sudo docker ps -q)
+
+# Kill all image-based
+# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/yolov5:latest)
+
+# Bash into running container
+# sudo docker exec -it 5a9b5863d93d bash
+
+# Bash into stopped container
+# id=$(sudo docker ps -qa) && sudo docker start $id && sudo docker exec -it $id bash
+
+# Clean up
+# docker system prune -a --volumes
+FROM python:3.9
+EXPOSE 8501
+WORKDIR /app
+COPY requirements.txt ./requirements.txt
+RUN pip3 install -r requirements.txt
+COPY . .
+# CMD streamlit run app.py
+CMD streamlit run --server.port $PORT app.py

LICENSE

@@ -0,0 +1,674 @@
+GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
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+ Everyone is permitted to copy and distribute verbatim copies
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+
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+<http://www.gnu.org/philosophy/why-not-lgpl.html>.

Procfile

@@ -0,0 +1 @@
+web: sh setup.sh && streamlit run app.py

README.md

@@ -1,46 +1,159 @@
----
-title: Planogram Compliance
-emoji: 👁
-colorFrom: gray
-colorTo: pink
-sdk: streamlit
-app_file: app.py
-pinned: false
-license: apache-2.0
----
+## <div align="center">Planogram Scoring</div>
+<p>
 
-# Configuration
+</p>
+- Train a Yolo Model on the available products in our data base to detect them on a shelf
+- https://wandb.ai/abhilash001vj/YOLOv5/runs/1v6yh7nk?workspace=user-abhilash001vj
+- Have the master planogram data captured as a matrix of products encoded as numbers (label encoding by looking the products names saved in a  list of all - the available product names )
+- Detect the products on real images from stores.
+- Arrange the detected products in the captured photograph to rows and columns 
+- Compare the product arrangement of captured photograph to the existing master planogram and produce the compliance score for correctly placed products
 
-`title`: _string_
-Display title for the Space
+</div>
 
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
+## <div align="center">YOLOv5</div>
+<p>
+YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents <a href="https://ultralytics.com">Ultralytics</a>
+ open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
+</p>
 
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+</div>
 
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+## <div align="center">Documentation</div>
 
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
+See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment.
 
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
+## <div align="center">Quick Start Examples</div>
 
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
+<details open>
+<summary>Install</summary>
 
-`models`: _List[string]_
-HF model IDs (like "gpt2" or "deepset/roberta-base-squad2") used in the Space.
-Will be parsed automatically from your code if not specified here.
+[**Python>=3.6.0**](https://www.python.org/) is required with all
+[requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including
+[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/):
+<!-- $ sudo apt update && apt install -y libgl1-mesa-glx libsm6 libxext6 libxrender-dev -->
 
-`datasets`: _List[string]_
-HF dataset IDs (like "common_voice" or "oscar-corpus/OSCAR-2109") used in the Space.
-Will be parsed automatically from your code if not specified here.
+```bash
+$ git clone https://github.com/ultralytics/yolov5
+$ cd yolov5
+$ pip install -r requirements.txt
+```
 
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
+</details>
+
+<details open>
+<summary>Inference</summary>
+
+Inference with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36). Models automatically download
+from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases).
+
+```python
+import torch
+
+# Model
+model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # or yolov5m, yolov5l, yolov5x, custom
+
+# Images
+img = 'https://ultralytics.com/images/zidane.jpg'  # or file, Path, PIL, OpenCV, numpy, list
+
+# Inference
+results = model(img)
+
+# Results
+results.print()  # or .show(), .save(), .crop(), .pandas(), etc.
+```
+
+</details>
+
+
+## <div align="center">Why YOLOv5</div>
+
+<p align="center"><img width="800" src="https://user-images.githubusercontent.com/26833433/114313216-f0a5e100-9af5-11eb-8445-c682b60da2e3.png"></p>
+<details>
+  <summary>YOLOv5-P5 640 Figure (click to expand)</summary>
+
+<p align="center"><img width="800" src="https://user-images.githubusercontent.com/26833433/114313219-f1d70e00-9af5-11eb-9973-52b1f98d321a.png"></p>
+</details>
+<details>
+  <summary>Figure Notes (click to expand)</summary>
+
+* GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size
+  32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS.
+* EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8.
+* **Reproduce** by
+  `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
+
+</details>
+
+### Pretrained Checkpoints
+
+[assets]: https://github.com/ultralytics/yolov5/releases
+
+|Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>test<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>V100 (ms) | |params<br><sup>(M) |FLOPs<br><sup>640 (B)
+|---                    |---  |---      |---      |---      |---     |---|---   |---
+|[YOLOv5s][assets]      |640  |36.7     |36.7     |55.4     |**2.0** |   |7.3   |17.0
+|[YOLOv5m][assets]      |640  |44.5     |44.5     |63.1     |2.7     |   |21.4  |51.3
+|[YOLOv5l][assets]      |640  |48.2     |48.2     |66.9     |3.8     |   |47.0  |115.4
+|[YOLOv5x][assets]      |640  |**50.4** |**50.4** |**68.8** |6.1     |   |87.7  |218.8
+|                       |     |         |         |         |        |   |      |
+|[YOLOv5s6][assets]     |1280 |43.3     |43.3     |61.9     |**4.3** |   |12.7  |17.4
+|[YOLOv5m6][assets]     |1280 |50.5     |50.5     |68.7     |8.4     |   |35.9  |52.4
+|[YOLOv5l6][assets]     |1280 |53.4     |53.4     |71.1     |12.3    |   |77.2  |117.7
+|[YOLOv5x6][assets]     |1280 |**54.4** |**54.4** |**72.0** |22.4    |   |141.8 |222.9
+|                       |     |         |         |         |        |   |      |
+|[YOLOv5x6][assets] TTA |1280 |**55.0** |**55.0** |**72.0** |70.8    |   |-     |-
+
+<details>
+  <summary>Table Notes (click to expand)</summary>
+
+* AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results
+  denote val2017 accuracy.
+* AP values are for single-model single-scale unless otherwise noted. **Reproduce mAP**
+  by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
+* Speed<sub>GPU</sub> averaged over 5000 COCO val2017 images using a
+  GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and
+  includes FP16 inference, postprocessing and NMS. **Reproduce speed**
+  by `python val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45 --half`
+* All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).
+* Test Time Augmentation ([TTA](https://github.com/ultralytics/yolov5/issues/303)) includes reflection and scale
+  augmentation. **Reproduce TTA** by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
+
+</details>
+
+## <div align="center">Contribute</div>
+
+We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see
+our [Contributing Guide](CONTRIBUTING.md) to get started.
+
+## <div align="center">Contact</div>
+
+For issues running YOLOv5 please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For business or
+professional support requests please visit [https://ultralytics.com/contact](https://ultralytics.com/contact).
+
+<br>
+
+<div align="center">
+    <a href="https://github.com/ultralytics">
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png" width="3%"/>
+    </a>
+    <img width="3%" />
+    <a href="https://www.linkedin.com/company/ultralytics">
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width="3%"/>
+    </a>
+    <img width="3%" />
+    <a href="https://twitter.com/ultralytics">
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width="3%"/>
+    </a>
+    <img width="3%" />
+    <a href="https://youtube.com/ultralytics">
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="3%"/>
+    </a>
+    <img width="3%" />
+    <a href="https://www.facebook.com/ultralytics">
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width="3%"/>
+    </a>
+    <img width="3%" />
+    <a href="https://www.instagram.com/ultralytics/">
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="3%"/>
+    </a>
+</div>

_requirements.txt

@@ -0,0 +1,36 @@
+# pip install -r requirements.txt
+streamlit
+# base ----------------------------------------
+# matplotlib>=3.2.2
+numpy>=1.18.5
+# opencv-python>=4.1.2
+# http://download.pytorch.org/whl/cpu/torch-1.7.1%2Bcpu-cp39-cp39-linux_x86_64.whl
+# gunicorn == 19.9.0
+# torchvision==0.2.2
+opencv-python-headless>=4.1.2
+Pillow>=8.0.0
+PyYAML>=5.3.1
+scipy>=1.4.1
+torch>=1.7.0
+torchvision>=0.8.1
+tqdm>=4.41.0
+
+# logging -------------------------------------
+# tensorboard>=2.4.1
+# wandb
+
+# plotting ------------------------------------
+# seaborn>=0.11.0
+pandas
+
+# export --------------------------------------
+# coremltools>=4.1
+# onnx>=1.9.0
+# scikit-learn==0.19.2  # for coreml quantization
+# tensorflow==2.4.1  # for TFLite export
+
+# extras --------------------------------------
+# Cython  # for pycocotools https://github.com/cocodataset/cocoapi/issues/172
+# pycocotools>=2.0  # COCO mAP
+# albumentations>=1.0.3
+# thop  # FLOPs computation

app.py

@@ -0,0 +1,296 @@
+# https://planogram-compliance.herokuapp.com/
+# https://dashboard.heroku.com/apps/planogram-compliance/deploy/heroku-git
+
+# https://medium.com/@mohcufe/how-to-deploy-your-trained-pytorch-model-on-heroku-ff4b73085ddd\
+# https://stackoverflow.com/questions/51730880/where-do-i-get-a-cpu-only-version-of-pytorch
+# https://blog.jcharistech.com/2020/02/26/how-to-deploy-a-face-detection-streamlit-app-on-heroku/
+# https://towardsdatascience.com/a-quick-tutorial-on-how-to-deploy-your-streamlit-app-to-heroku-
+# https://www.analyticsvidhya.com/blog/2021/06/deploy-your-ml-dl-streamlit-application-on-heroku/
+# https://gist.github.com/jeremyjordan/6b506257509e8ba673f145baa568a1ea
+
+import json
+
+# https://www.r-bloggers.com/2020/12/creating-a-streamlit-web-app-building-with-docker-github-actions-and-hosting-on-heroku/
+# https://devcenter.heroku.com/articles/container-registry-and-runtime
+# from yolo_inference_util import run_yolo_v5
+import os
+from tempfile import NamedTemporaryFile
+
+import cv2
+import numpy as np
+import pandas as pd
+import streamlit as st
+
+# import matplotlib.pyplot as plt
+from app_utils import annotate_planogram_compliance, bucket_sort, do_sorting, xml_to_csv
+from inference import run
+
+# from utils.plots import Annotator, colors
+# from utils.general import scale_coords
+
+app_formal_name = "Planogram Compliance"
+
+FILE_UPLOAD_DIR = "tmp"
+
+os.makedirs(FILE_UPLOAD_DIR, exist_ok=True)
+# Start the app in wide-mode
+st.set_page_config(
+    layout="wide",
+    page_title=app_formal_name,
+)
+# https://github.com/streamlit/streamlit/issues/1361
+uploaded_file = st.file_uploader(
+    "Choose a planogram image to score",
+    type=["jpg", "JPEG", "PNG", "JPG", "jpeg"],
+)
+uploaded_master_planogram_file = st.file_uploader(
+    "Upload a master planogram", type=["jpg", "JPEG", "PNG", "JPG", "jpeg"]
+)
+annotation_file = st.file_uploader("upload master polanogram", type=["xml"])
+temp_file = NamedTemporaryFile(delete=False)
+
+target_names = [
+    "Bottle,100PLUS ACTIVE 1.5L",
+    "Bottle,100PLUS ACTIVE 500ML",
+    "Bottle,100PLUS LEMON LIME 1.5L",
+    "Bottle,100PLUS ORANGE 500ML",
+    "Bottle,100PLUS ORIGINAL 1.5L",
+    "Bottle,100PLUS TANGY ORANGE 1.5L",
+    "Bottle,100PLUS ZERO 1.5L",
+    "Bottle,100PLUS ZERO 500ML",
+    "Packet,F:M MAGNOLIA CHOC 1L",
+    "Bottle,F&N GINGER ADE 1.5L",
+    "Bottle,F&N GRAPE 1.5L",
+    "Bottle,F&N ICE CREAM SODA 1.5L",
+    "Bottle,F&N LYCHEE PEAR 1.5L",
+    "Bottle,F&N ORANGE 1.5L",
+    "Bottle,F&N PINEAPPLE PET 1.5L",
+    "Bottle,F&N SARSI 1.5L",
+    "Bottle,F&N SS ICE LEM TEA RS 500ML",
+    "Bottle,F&N SS ICE LEMON TEA RS 1.5L",
+    "Bottle,F&N SS ICE LEMON TEA 1.5L",
+    "Bottle,F&N SS ICE LEMON TEA 500ML",
+    "Bottle,F&N SS ICE PEACH TEA 1.5L",
+    "Bottle,SS ICE LEMON GT 1.48L",
+    "Bottle,SS WHITE CHRYS TEA 1.48L",
+    "Packet,FARMHOUSE FRESH MILK 1L FNDM",
+    "Packet,FARMHOUSE PLAIN LF 1L",
+    "Packet,PURA FRESH MILK 1L FS",
+    "Packet,NUTRISOY REG NO SUGAR ADDED 1L",
+    "Packet,NUTRISOY PLAIN 475ML",
+    "Packet,NUTRISOY PLAIN 1L",
+    "Packet,NUTRISOY OMEGA RD SUGAR 1L",
+    "Packet,NUTRISOY OMEGA NSA 1L",
+    "Packet,NUTRISOY ALMOND 1L",
+    "Packet,MAGNOLIA FRESH MILK 1L FNDM",
+    "Packet,FM MAG FC PLAIN 200ML",
+    "Packet,MAG OMEGA PLUS PLAIN 200ML",
+    "Packet,MAG KURMA MILK 500ML",
+    "Packet,MAG KURMA MILK 1L",
+    "Packet,MAG CHOCOLATE FC 500ML",
+    "Packet,MAG BROWN SUGAR SS MILK 1L",
+    "Packet,FM MAG LFHC PLN 500ML",
+    "Packet,FM MAG LFHC OAT 500ML",
+    "Packet,FM MAG LFHC OAT 1L",
+    "Packet,FM MAG FC PLAIN 500ML",
+    "Void,PARTIAL VOID",
+    "Void,FULL VOID",
+    "Bottle,F&N SS ICE LEM TEA 500ML",
+]
+
+run_app = st.button("Run the compliance check")
+if run_app and uploaded_file is not None:
+    # Convert the file to an opencv image.
+    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+    temp_file.write(uploaded_file.getvalue())
+    uploaded_img = cv2.imdecode(file_bytes, 1)
+    cv2.imwrite("tmp/to_score_planogram_tmp.png", uploaded_img)
+
+    # if uploaded_master_planogram_file is None:
+    #     master = cv2.imread('./sample_master_planogram.jpeg')
+
+    names_dict = {name: id for id, name in enumerate(target_names)}
+
+    sorted_xml_df = None
+    # https://discuss.streamlit.io/t/unable-to-read-files-using-standard-file-uploader/2258/2
+    if uploaded_master_planogram_file and annotation_file:
+        file_bytes = np.asarray(
+            bytearray(uploaded_master_planogram_file.read()), dtype=np.uint8
+        )
+        master = cv2.imdecode(file_bytes, 1)
+        cv2.imwrite("tmp/master_tmp.png", master)
+        # cv2.imwrite("tmp_uploaded_master_planogram_img.png", master)
+        # xml = annotation_file.read()
+        # tmp_xml ="tmp_xml_annotation.xml"
+        # with open(tmp_xml ,'w',encoding='utf-8') as f:
+        #      xml = f.write(xml)
+        xml_df = xml_to_csv(annotation_file)
+        xml_df["cls"] = xml_df["cls"].map(names_dict)
+        sorted_xml_df = do_sorting(xml_df)
+        sorted_xml_df.line_number.value_counts()
+
+        line_data = sorted_xml_df.line_number.value_counts()
+        n_rows = int(len(line_data))
+        n_cols = int(max(line_data))
+        master_table = np.zeros((n_rows, n_cols)) + 101
+        master_annotations = []
+        for i, row in sorted_xml_df.groupby("line_number"):
+            # print(f"Adding products in the row {i} to the detected planogram", row.cls.tolist())
+            products = row.cls.tolist()
+            master_table[int(i - 1), 0 : len(products)] = products
+            annotations = [
+                (int(k), int(v))
+                for k, v in list(
+                    zip(row.cls.unique(), row.cls.value_counts().tolist())
+                )
+            ]
+            master_annotations.append(annotations)
+        master_table.shape
+        # print("Annoatated planogram")
+        # print(np.matrix(master_table))
+
+    elif uploaded_master_planogram_file:
+        print(
+            "Finding the amster annotations with the YOLOv5 model predictions"
+        )
+        file_bytes = np.asarray(
+            bytearray(uploaded_master_planogram_file.read()), dtype=np.uint8
+        )
+        master = cv2.imdecode(file_bytes, 1)
+        cv2.imwrite("tmp/master_tmp.png", master)
+        master_results = run(
+            weights="base_line_best_model_exp5.pt",
+            source="tmp/master_tmp.png",
+            imgsz=[640, 640],
+            conf_thres=0.6,
+            iou_thres=0.6,
+        )
+
+        bb_df = pd.DataFrame(
+            master_results[0][1].tolist(),
+            columns=["xmin", "ymin", "xmax", "ymax", "conf", "cls"],
+        )
+        sorted_df = do_sorting(bb_df)
+
+        n_rows = int(sorted_df.line_number.max())
+        n_cols = int(
+            sorted_df.groupby("line_number")
+            .size()
+            .reset_index(name="counts")["counts"]
+            .max()
+        )
+        non_null_product = 101
+        print("master size", n_rows, n_cols)
+        master_annotations = []
+        master_table = np.zeros((int(n_rows), int(n_cols))) + non_null_product
+        for i, row in sorted_df.groupby("line_number"):
+            # print(f"Adding products in the row {i} to the detected planogram", row.cls.tolist())
+            products = row.cls.tolist()
+            col_len = min(len(products), n_cols)
+            print("col size: ", col_len)
+            print("row size: ", i - 1)
+            if n_rows <= (i - 1):
+                print("more rows than expected in the predictions")
+                break
+            master_table[int(i - 1), 0:col_len] = products[:col_len]
+            annotations = [
+                (int(k), int(v))
+                for k, v in list(
+                    zip(row.cls.unique(), row.cls.value_counts().tolist())
+                )
+            ]
+            master_annotations.append(annotations)
+    else:
+        master = cv2.imread("./sample_master_planogram.jpeg")
+        n_rows = 3
+        n_cols = 16
+        master_table = np.zeros((n_rows, n_cols)) + 101
+        master_annotations = [
+            [(32, 12), (8, 4)],
+            [(36, 1), (41, 6), (50, 4), (51, 3), (52, 2)],
+            [(23, 5), (24, 6), (54, 5)],
+        ]
+
+        for i, row in enumerate(master_annotations):
+            idx = 0
+            for product, count in row:
+                master_table[i, idx : idx + count] = product
+                idx = idx + count
+    # Now do something with the image! For example, let's display it:
+    # st.image(opencv_image, channels="BGR")
+
+    # uploaded_img = '/content/drive/My Drive/0.CV/0.Planogram_Compliance/planogram_data/images/test/IMG_5718.jpg'
+    result_list = run(
+        weights="base_line_best_model_exp5.pt",
+        source="tmp/to_score_planogram_tmp.png",
+        imgsz=[640, 640],
+        conf_thres=0.6,
+        iou_thres=0.6,
+    )
+
+    bb_df = pd.DataFrame(
+        result_list[0][1].tolist(),
+        columns=["xmin", "ymin", "xmax", "ymax", "conf", "cls"],
+    )
+    sorted_df = do_sorting(bb_df)
+
+    non_null_product = 101
+    print("master size", n_rows, n_cols)
+    detected_table = np.zeros((n_rows, n_cols)) + non_null_product
+    for i, row in sorted_df.groupby("line_number"):
+        # print(f"Adding products in the row {i} to the detected planogram", row.cls.tolist())
+        products = row.cls.tolist()
+        col_len = min(len(products), n_cols)
+        print("col size: ", col_len)
+        print("row size: ", i - 1)
+        if n_rows <= (i - 1):
+            print("more rows than expected in the predictions")
+            break
+        detected_table[int(i - 1), 0:col_len] = products[:col_len]
+
+    # score = (master_table == detected_table).sum() / (master_table != non_null_product).sum()
+    correct_matches = (
+        np.ma.masked_equal(master_table, non_null_product) == detected_table
+    ).sum()
+    total_products = (master_table != non_null_product).sum()
+    score = correct_matches / total_products
+    # if sorted_xml_df is not None:
+    #     annotate_df = sorted_xml_df[["xmin","ymin", "xmax", "ymax", "line_number","cls"]].astype(int)
+    # else:
+    annotate_df = sorted_df[
+        ["xmin", "ymin", "xmax", "ymax", "line_number", "cls"]
+    ].astype(int)
+
+    mask = master_table != non_null_product
+    m_detected_table = np.ma.masked_array(master_table, mask=mask)
+    m_annotated_table = np.ma.masked_array(detected_table, mask=mask)
+
+    #  wrong_indexes = np.ravel_multi_index(master_table*mask != detected_table*mask, master_table.shape)
+    wrong_indexes = np.where(master_table != detected_table)
+    correct_indexes = np.where(master_table == detected_table)
+    annotated_planogram = annotate_planogram_compliance(
+        uploaded_img, annotate_df, correct_indexes, wrong_indexes, target_names
+    )
+    st.title("Target Products")
+    st.write(json.dumps(target_names))
+    st.title("The master planogram annotation")
+    st.write(
+        "The annotations are based on the index of products from Target products list "
+    )
+    st.write(json.dumps(master_annotations))
+
+    # https://github.com/streamlit/streamlit/issues/888
+    st.image(
+        [master, annotated_planogram, result_list[0][0]],
+        width=512,
+        caption=[
+            "Master planogram",
+            "Planogram Compliance",
+            "Planogram Predictions",
+        ],
+        channels="BGR",
+    )
+    # st.image([master, annotated_planogram], width=512, caption=["Master planogram",  "Planogram Compliance"],  channels="BGR")
+    st.title("Planogram Compiance score")
+    # st.write(f"{correct_matches} / {total_products}")
+    st.write(score)

app_utils.py

@@ -0,0 +1,196 @@
+import glob
+import json
+import os
+import xml.etree.ElementTree as ET
+
+import cv2
+
+# from sklearn.externals import joblib
+import joblib
+import numpy as np
+import pandas as pd
+
+# from .variables import old_ocr_req_cols
+# from .skew_correction import  PageSkewWraper
+
+const_HW = 1.294117647
+const_W = 600
+# https://www.forbes.com/sites/forbestechcouncil/2020/06/02/leveraging-technologies-to-align-realograms-and-planograms-for-grocery/?sh=506b8b78e86c
+
+
+# https://stackoverflow.com/questions/39403183/python-opencv-sorting-contours
+# http://devdoc.net/linux/OpenCV-3.2.0/da/d0c/tutorial_bounding_rects_circles.html
+# https://stackoverflow.com/questions/10297713/find-contour-of-the-set-of-points-in-opencv
+# https://stackoverflow.com/questions/16538774/dealing-with-contours-and-bounding-rectangle-in-opencv-2-4-python-2-7
+# https://stackoverflow.com/questions/50308055/creating-bounding-boxes-for-contours
+# https://stackoverflow.com/questions/57296398/how-can-i-get-better-results-of-bounding-box-using-find-contours-of-opencv
+# http://amroamroamro.github.io/mexopencv/opencv/generalContours_demo1.html
+# https://gist.github.com/bigsnarfdude/d811e31ee17495f82f10db12651ae82d
+# http://man.hubwiz.com/docset/OpenCV.docset/Contents/Resources/Documents/da/d0c/tutorial_bounding_rects_circles.html
+# https://www.analyticsvidhya.com/blog/2021/05/document-layout-detection-and-ocr-with-detectron2/
+# https://colab.research.google.com/drive/1m6gaQF6Q4M0IaSjoo_4jWllKJjK-i6fw?usp=sharing#scrollTo=lEyl3wYKHAe1
+# https://stackoverflow.com/questions/39403183/python-opencv-sorting-contours
+# https://docs.opencv.org/2.4/doc/tutorials/imgproc/shapedescriptors/bounding_rects_circles/bounding_rects_circles.html
+# https://www.pyimagesearch.com/2016/03/21/ordering-coordinates-clockwise-with-python-and-opencv/
+
+
+def bucket_sort(df, colmn, ymax_col="ymax", ymin_col="ymin"):
+    df["line_number"] = 0
+    colmn.append("line_number")
+    array_value = df[colmn].values
+    start_index = Line_counter = counter = 0
+    ymax, ymin, line_no = (
+        colmn.index(ymax_col),
+        colmn.index(ymin_col),
+        colmn.index("line_number"),
+    )
+    while counter < len(array_value):
+        current_ymax = array_value[start_index][ymax]
+        for next_index in range(start_index, len(array_value)):
+            counter += 1
+
+            next_ymin = array_value[next_index][ymin]
+            next_ymax = array_value[next_index][ymax]
+            if current_ymax > next_ymin:
+
+                array_value[next_index][line_no] = Line_counter + 1
+            #                 if current_ymax < next_ymax:
+
+            #                     current_ymax = next_ymax
+            else:
+                counter -= 1
+                break
+        # print(counter, len(array_value), start_index)
+        start_index = counter
+        Line_counter += 1
+    return pd.DataFrame(array_value, columns=colmn)
+
+
+def do_sorting(df):
+    df.sort_values(["ymin", "xmin"], ascending=True, inplace=True)
+    df["idx"] = df.index
+    if "line_number" in df.columns:
+        print("line number removed")
+        df.drop("line_number", axis=1, inplace=True)
+    req_colns = ["xmin", "ymin", "xmax", "ymax", "idx"]
+    temp_df = df.copy()
+    temp = bucket_sort(temp_df.copy(), req_colns)
+    df = df.merge(temp[["idx", "line_number"]], on="idx")
+    df.sort_values(["line_number", "xmin"], ascending=True, inplace=True)
+    df = df.reset_index(drop=True)
+    df = df.reset_index(drop=True)
+    return df
+
+
+def xml_to_csv(xml_file):
+    # https://gist.github.com/rotemtam/88d9a4efae243fc77ed4a0f9917c8f6c
+    xml_list = []
+    # for xml_file in glob.glob(path + '/*.xml'):
+    # https://discuss.streamlit.io/t/unable-to-read-files-using-standard-file-uploader/2258/2
+    tree = ET.parse(xml_file)
+    root = tree.getroot()
+    for member in root.findall("object"):
+        bbx = member.find("bndbox")
+        xmin = int(bbx.find("xmin").text)
+        ymin = int(bbx.find("ymin").text)
+        xmax = int(bbx.find("xmax").text)
+        ymax = int(bbx.find("ymax").text)
+        label = member.find("name").text
+
+        value = (
+            root.find("filename").text,
+            int(root.find("size")[0].text),
+            int(root.find("size")[1].text),
+            label,
+            xmin,
+            ymin,
+            xmax,
+            ymax,
+        )
+        xml_list.append(value)
+    column_name = [
+        "filename",
+        "width",
+        "height",
+        "cls",
+        "xmin",
+        "ymin",
+        "xmax",
+        "ymax",
+    ]
+    xml_df = pd.DataFrame(xml_list, columns=column_name)
+    return xml_df
+
+
+# def annotate_planogram_compliance(img0, sorted_xml_df, wrong_indexes, target_names):
+#     # annotator = Annotator(img0, line_width=3, pil=True)
+#     det = sorted_xml_df[['xmin', 'ymin', 'xmax', 'ymax','cls']].values
+#     # det[:, :4] = scale_coords((640, 640), det[:, :4], img0.shape).round()
+#     for i, (*xyxy, cls) in enumerate(det):
+
+#         c = int(cls)  # integer class
+
+#         if i in wrong_indexes:
+#             # print(xyxy, "Wrong detection", (255, 0, 0))
+#             label =  "Wrong detection"
+#             color = (0,0,255)
+#         else:
+#             # print(xyxy, label, (0, 255, 0))
+#             label = f'{target_names[c]}'
+#             color = (0,255, 0)
+#         org = (int(xyxy[0]), int(xyxy[1]) )
+#         top_left = org
+#         bottom_right = (int(xyxy[2]), int(xyxy[3]))
+#         # print("#"*50)
+#         # print(f"Anooatting cv2 rectangle with shape: { img0.shape}, top left: { top_left}, bottom right: { bottom_right} , color : { color },  thickness: {3}, cv2.LINE_8")
+#         # print("#"*50)
+#         cv2.rectangle(img0, top_left, bottom_right , color,  3, cv2.LINE_8)
+
+#         cv2.putText(img0, label, tuple(org), cv2. FONT_HERSHEY_SIMPLEX  , 0.5, color)
+
+#     return img0
+
+
+def annotate_planogram_compliance(
+    img0, sorted_df, correct_indexes, wrong_indexes, target_names
+):
+    # annotator = Annotator(img0, line_width=3, pil=True)
+    det = sorted_df[["xmin", "ymin", "xmax", "ymax", "cls"]].values
+    # det[:, :4] = scale_coords((640, 640), det[:, :4], img0.shape).round()
+    for x, y in zip(*correct_indexes):
+        try:
+            row = sorted_df[sorted_df["line_number"] == x + 1].iloc[y]
+            xyxy = row[["xmin", "ymin", "xmax", "ymax"]].values
+            label = f'{target_names[row["cls"]]}'
+            color = (0, 255, 0)
+            # org = (int(xyxy[0]), int(xyxy[1]) )
+            top_left = (int(row["xmin"]), int(row["ymin"]))
+            bottom_right = (int(row["xmax"]), int(row["ymax"]))
+            cv2.rectangle(img0, top_left, bottom_right, color, 3, cv2.LINE_8)
+
+            cv2.putText(
+                img0, label, top_left, cv2.FONT_HERSHEY_SIMPLEX, 0.5, color
+            )
+        except Exception as e:
+            print("Error: " + str(e))
+            continue
+
+    for x, y in zip(*wrong_indexes):
+        try:
+            row = sorted_df[sorted_df["line_number"] == x + 1].iloc[y]
+            xyxy = row[["xmin", "ymin", "xmax", "ymax"]].values
+            label = f'{target_names[row["cls"]]}'
+            color = (0, 0, 255)
+            # org = (int(xyxy[0]), int(xyxy[1]) )
+            top_left = (row["xmin"], row["ymin"])
+            bottom_right = (row["xmax"], row["ymax"])
+            cv2.rectangle(img0, top_left, bottom_right, color, 3, cv2.LINE_8)
+
+            cv2.putText(
+                img0, label, top_left, cv2.FONT_HERSHEY_SIMPLEX, 0.5, color
+            )
+        except Exception as e:
+            print("Error: " + str(e))
+            continue
+
+    return img0

base_line_best_model_exp5.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c259d5e97010ee1c9775d6d8c3bc8bb73f52a5ad871ca920902f35563f2acb42
+size 14621601

best_sku_model.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:46627e4923a4cbb695e2f1da5944ec7e2930acb640b822227aab334bddf1548b
+size 14355573

classify/predict.py

@@ -0,0 +1,345 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Run YOLOv5 classification inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
+
+Usage - sources:
+    $ python classify/predict.py --weights yolov5s-cls.pt --source 0                               # webcam
+                                                                   img.jpg                         # image
+                                                                   vid.mp4                         # video
+                                                                   screen                          # screenshot
+                                                                   path/                           # directory
+                                                                   list.txt                        # list of images
+                                                                   list.streams                    # list of streams
+                                                                   'path/*.jpg'                    # glob
+                                                                   'https://youtu.be/Zgi9g1ksQHc'  # YouTube
+                                                                   'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
+
+Usage - formats:
+    $ python classify/predict.py --weights yolov5s-cls.pt                 # PyTorch
+                                           yolov5s-cls.torchscript        # TorchScript
+                                           yolov5s-cls.onnx               # ONNX Runtime or OpenCV DNN with --dnn
+                                           yolov5s-cls_openvino_model     # OpenVINO
+                                           yolov5s-cls.engine             # TensorRT
+                                           yolov5s-cls.mlmodel            # CoreML (macOS-only)
+                                           yolov5s-cls_saved_model        # TensorFlow SavedModel
+                                           yolov5s-cls.pb                 # TensorFlow GraphDef
+                                           yolov5s-cls.tflite             # TensorFlow Lite
+                                           yolov5s-cls_edgetpu.tflite     # TensorFlow Edge TPU
+                                           yolov5s-cls_paddle_model       # PaddlePaddle
+"""
+
+import argparse
+import os
+import platform
+import sys
+from pathlib import Path
+
+import torch
+import torch.nn.functional as F
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[1]  # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
+
+from models.common import DetectMultiBackend
+from utils.augmentations import classify_transforms
+from utils.dataloaders import (
+    IMG_FORMATS,
+    VID_FORMATS,
+    LoadImages,
+    LoadScreenshots,
+    LoadStreams,
+)
+from utils.general import (
+    LOGGER,
+    Profile,
+    check_file,
+    check_img_size,
+    check_imshow,
+    check_requirements,
+    colorstr,
+    cv2,
+    increment_path,
+    print_args,
+    strip_optimizer,
+)
+from utils.plots import Annotator
+from utils.torch_utils import select_device, smart_inference_mode
+
+
+@smart_inference_mode()
+def run(
+    weights=ROOT / "yolov5s-cls.pt",  # model.pt path(s)
+    source=ROOT / "data/images",  # file/dir/URL/glob/screen/0(webcam)
+    data=ROOT / "data/coco128.yaml",  # dataset.yaml path
+    imgsz=(224, 224),  # inference size (height, width)
+    device="",  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+    view_img=False,  # show results
+    save_txt=False,  # save results to *.txt
+    nosave=False,  # do not save images/videos
+    augment=False,  # augmented inference
+    visualize=False,  # visualize features
+    update=False,  # update all models
+    project=ROOT / "runs/predict-cls",  # save results to project/name
+    name="exp",  # save results to project/name
+    exist_ok=False,  # existing project/name ok, do not increment
+    half=False,  # use FP16 half-precision inference
+    dnn=False,  # use OpenCV DNN for ONNX inference
+    vid_stride=1,  # video frame-rate stride
+):
+    source = str(source)
+    save_img = not nosave and not source.endswith(
+        ".txt"
+    )  # save inference images
+    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
+    is_url = source.lower().startswith(
+        ("rtsp://", "rtmp://", "http://", "https://")
+    )
+    webcam = (
+        source.isnumeric()
+        or source.endswith(".streams")
+        or (is_url and not is_file)
+    )
+    screenshot = source.lower().startswith("screen")
+    if is_url and is_file:
+        source = check_file(source)  # download
+
+    # Directories
+    save_dir = increment_path(
+        Path(project) / name, exist_ok=exist_ok
+    )  # increment run
+    (save_dir / "labels" if save_txt else save_dir).mkdir(
+        parents=True, exist_ok=True
+    )  # make dir
+
+    # Load model
+    device = select_device(device)
+    model = DetectMultiBackend(
+        weights, device=device, dnn=dnn, data=data, fp16=half
+    )
+    stride, names, pt = model.stride, model.names, model.pt
+    imgsz = check_img_size(imgsz, s=stride)  # check image size
+
+    # Dataloader
+    bs = 1  # batch_size
+    if webcam:
+        view_img = check_imshow(warn=True)
+        dataset = LoadStreams(
+            source,
+            img_size=imgsz,
+            transforms=classify_transforms(imgsz[0]),
+            vid_stride=vid_stride,
+        )
+        bs = len(dataset)
+    elif screenshot:
+        dataset = LoadScreenshots(
+            source, img_size=imgsz, stride=stride, auto=pt
+        )
+    else:
+        dataset = LoadImages(
+            source,
+            img_size=imgsz,
+            transforms=classify_transforms(imgsz[0]),
+            vid_stride=vid_stride,
+        )
+    vid_path, vid_writer = [None] * bs, [None] * bs
+
+    # Run inference
+    model.warmup(imgsz=(1 if pt else bs, 3, *imgsz))  # warmup
+    seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
+    for path, im, im0s, vid_cap, s in dataset:
+        with dt[0]:
+            im = torch.Tensor(im).to(model.device)
+            im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
+            if len(im.shape) == 3:
+                im = im[None]  # expand for batch dim
+
+        # Inference
+        with dt[1]:
+            results = model(im)
+
+        # Post-process
+        with dt[2]:
+            pred = F.softmax(results, dim=1)  # probabilities
+
+        # Process predictions
+        for i, prob in enumerate(pred):  # per image
+            seen += 1
+            if webcam:  # batch_size >= 1
+                p, im0, frame = path[i], im0s[i].copy(), dataset.count
+                s += f"{i}: "
+            else:
+                p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0)
+
+            p = Path(p)  # to Path
+            save_path = str(save_dir / p.name)  # im.jpg
+            txt_path = str(save_dir / "labels" / p.stem) + (
+                "" if dataset.mode == "image" else f"_{frame}"
+            )  # im.txt
+
+            s += "%gx%g " % im.shape[2:]  # print string
+            annotator = Annotator(im0, example=str(names), pil=True)
+
+            # Print results
+            top5i = prob.argsort(0, descending=True)[
+                :5
+            ].tolist()  # top 5 indices
+            s += f"{', '.join(f'{names[j]} {prob[j]:.2f}' for j in top5i)}, "
+
+            # Write results
+            text = "\n".join(f"{prob[j]:.2f} {names[j]}" for j in top5i)
+            if save_img or view_img:  # Add bbox to image
+                annotator.text((32, 32), text, txt_color=(255, 255, 255))
+            if save_txt:  # Write to file
+                with open(f"{txt_path}.txt", "a") as f:
+                    f.write(text + "\n")
+
+            # Stream results
+            im0 = annotator.result()
+            if view_img:
+                if platform.system() == "Linux" and p not in windows:
+                    windows.append(p)
+                    cv2.namedWindow(
+                        str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO
+                    )  # allow window resize (Linux)
+                    cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
+                cv2.imshow(str(p), im0)
+                cv2.waitKey(1)  # 1 millisecond
+
+            # Save results (image with detections)
+            if save_img:
+                if dataset.mode == "image":
+                    cv2.imwrite(save_path, im0)
+                else:  # 'video' or 'stream'
+                    if vid_path[i] != save_path:  # new video
+                        vid_path[i] = save_path
+                        if isinstance(vid_writer[i], cv2.VideoWriter):
+                            vid_writer[
+                                i
+                            ].release()  # release previous video writer
+                        if vid_cap:  # video
+                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
+                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+                        else:  # stream
+                            fps, w, h = 30, im0.shape[1], im0.shape[0]
+                        save_path = str(
+                            Path(save_path).with_suffix(".mp4")
+                        )  # force *.mp4 suffix on results videos
+                        vid_writer[i] = cv2.VideoWriter(
+                            save_path,
+                            cv2.VideoWriter_fourcc(*"mp4v"),
+                            fps,
+                            (w, h),
+                        )
+                    vid_writer[i].write(im0)
+
+        # Print time (inference-only)
+        LOGGER.info(f"{s}{dt[1].dt * 1E3:.1f}ms")
+
+    # Print results
+    t = tuple(x.t / seen * 1e3 for x in dt)  # speeds per image
+    LOGGER.info(
+        f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}"
+        % t
+    )
+    if save_txt or save_img:
+        s = (
+            f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}"
+            if save_txt
+            else ""
+        )
+        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
+    if update:
+        strip_optimizer(
+            weights[0]
+        )  # update model (to fix SourceChangeWarning)
+
+
+def parse_opt():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--weights",
+        nargs="+",
+        type=str,
+        default=ROOT / "yolov5s-cls.pt",
+        help="model path(s)",
+    )
+    parser.add_argument(
+        "--source",
+        type=str,
+        default=ROOT / "data/images",
+        help="file/dir/URL/glob/screen/0(webcam)",
+    )
+    parser.add_argument(
+        "--data",
+        type=str,
+        default=ROOT / "data/coco128.yaml",
+        help="(optional) dataset.yaml path",
+    )
+    parser.add_argument(
+        "--imgsz",
+        "--img",
+        "--img-size",
+        nargs="+",
+        type=int,
+        default=[224],
+        help="inference size h,w",
+    )
+    parser.add_argument(
+        "--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu"
+    )
+    parser.add_argument("--view-img", action="store_true", help="show results")
+    parser.add_argument(
+        "--save-txt", action="store_true", help="save results to *.txt"
+    )
+    parser.add_argument(
+        "--nosave", action="store_true", help="do not save images/videos"
+    )
+    parser.add_argument(
+        "--augment", action="store_true", help="augmented inference"
+    )
+    parser.add_argument(
+        "--visualize", action="store_true", help="visualize features"
+    )
+    parser.add_argument(
+        "--update", action="store_true", help="update all models"
+    )
+    parser.add_argument(
+        "--project",
+        default=ROOT / "runs/predict-cls",
+        help="save results to project/name",
+    )
+    parser.add_argument(
+        "--name", default="exp", help="save results to project/name"
+    )
+    parser.add_argument(
+        "--exist-ok",
+        action="store_true",
+        help="existing project/name ok, do not increment",
+    )
+    parser.add_argument(
+        "--half", action="store_true", help="use FP16 half-precision inference"
+    )
+    parser.add_argument(
+        "--dnn", action="store_true", help="use OpenCV DNN for ONNX inference"
+    )
+    parser.add_argument(
+        "--vid-stride", type=int, default=1, help="video frame-rate stride"
+    )
+    opt = parser.parse_args()
+    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
+    print_args(vars(opt))
+    return opt
+
+
+def main(opt):
+    check_requirements(exclude=("tensorboard", "thop"))
+    run(**vars(opt))
+
+
+if __name__ == "__main__":
+    opt = parse_opt()
+    main(opt)

classify/train.py

@@ -0,0 +1,537 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Train a YOLOv5 classifier model on a classification dataset
+
+Usage - Single-GPU training:
+    $ python classify/train.py --model yolov5s-cls.pt --data imagenette160 --epochs 5 --img 224
+
+Usage - Multi-GPU DDP training:
+    $ python -m torch.distributed.run --nproc_per_node 4 --master_port 2022 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
+
+Datasets:           --data mnist, fashion-mnist, cifar10, cifar100, imagenette, imagewoof, imagenet, or 'path/to/data'
+YOLOv5-cls models:  --model yolov5n-cls.pt, yolov5s-cls.pt, yolov5m-cls.pt, yolov5l-cls.pt, yolov5x-cls.pt
+Torchvision models: --model resnet50, efficientnet_b0, etc. See https://pytorch.org/vision/stable/models.html
+"""
+
+import argparse
+import os
+import subprocess
+import sys
+import time
+from copy import deepcopy
+from datetime import datetime
+from pathlib import Path
+
+import torch
+import torch.distributed as dist
+import torch.hub as hub
+import torch.optim.lr_scheduler as lr_scheduler
+import torchvision
+from torch.cuda import amp
+from tqdm import tqdm
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[1]  # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
+
+from classify import val as validate
+from models.experimental import attempt_load
+from models.yolo import ClassificationModel, DetectionModel
+from utils.dataloaders import create_classification_dataloader
+from utils.general import (
+    DATASETS_DIR,
+    LOGGER,
+    TQDM_BAR_FORMAT,
+    WorkingDirectory,
+    check_git_info,
+    check_git_status,
+    check_requirements,
+    colorstr,
+    download,
+    increment_path,
+    init_seeds,
+    print_args,
+    yaml_save,
+)
+from utils.loggers import GenericLogger
+from utils.plots import imshow_cls
+from utils.torch_utils import (
+    ModelEMA,
+    model_info,
+    reshape_classifier_output,
+    select_device,
+    smart_DDP,
+    smart_optimizer,
+    smartCrossEntropyLoss,
+    torch_distributed_zero_first,
+)
+
+LOCAL_RANK = int(
+    os.getenv("LOCAL_RANK", -1)
+)  # https://pytorch.org/docs/stable/elastic/run.html
+RANK = int(os.getenv("RANK", -1))
+WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1))
+GIT_INFO = check_git_info()
+
+
+def train(opt, device):
+    init_seeds(opt.seed + 1 + RANK, deterministic=True)
+    save_dir, data, bs, epochs, nw, imgsz, pretrained = (
+        opt.save_dir,
+        Path(opt.data),
+        opt.batch_size,
+        opt.epochs,
+        min(os.cpu_count() - 1, opt.workers),
+        opt.imgsz,
+        str(opt.pretrained).lower() == "true",
+    )
+    cuda = device.type != "cpu"
+
+    # Directories
+    wdir = save_dir / "weights"
+    wdir.mkdir(parents=True, exist_ok=True)  # make dir
+    last, best = wdir / "last.pt", wdir / "best.pt"
+
+    # Save run settings
+    yaml_save(save_dir / "opt.yaml", vars(opt))
+
+    # Logger
+    logger = (
+        GenericLogger(opt=opt, console_logger=LOGGER)
+        if RANK in {-1, 0}
+        else None
+    )
+
+    # Download Dataset
+    with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT):
+        data_dir = data if data.is_dir() else (DATASETS_DIR / data)
+        if not data_dir.is_dir():
+            LOGGER.info(
+                f"\nDataset not found ⚠️, missing path {data_dir}, attempting download..."
+            )
+            t = time.time()
+            if str(data) == "imagenet":
+                subprocess.run(
+                    f"bash {ROOT / 'data/scripts/get_imagenet.sh'}",
+                    shell=True,
+                    check=True,
+                )
+            else:
+                url = f"https://github.com/ultralytics/yolov5/releases/download/v1.0/{data}.zip"
+                download(url, dir=data_dir.parent)
+            s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {colorstr('bold', data_dir)}\n"
+            LOGGER.info(s)
+
+    # Dataloaders
+    nc = len(
+        [x for x in (data_dir / "train").glob("*") if x.is_dir()]
+    )  # number of classes
+    trainloader = create_classification_dataloader(
+        path=data_dir / "train",
+        imgsz=imgsz,
+        batch_size=bs // WORLD_SIZE,
+        augment=True,
+        cache=opt.cache,
+        rank=LOCAL_RANK,
+        workers=nw,
+    )
+
+    test_dir = (
+        data_dir / "test" if (data_dir / "test").exists() else data_dir / "val"
+    )  # data/test or data/val
+    if RANK in {-1, 0}:
+        testloader = create_classification_dataloader(
+            path=test_dir,
+            imgsz=imgsz,
+            batch_size=bs // WORLD_SIZE * 2,
+            augment=False,
+            cache=opt.cache,
+            rank=-1,
+            workers=nw,
+        )
+
+    # Model
+    with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT):
+        if Path(opt.model).is_file() or opt.model.endswith(".pt"):
+            model = attempt_load(opt.model, device="cpu", fuse=False)
+        elif (
+            opt.model in torchvision.models.__dict__
+        ):  # TorchVision models i.e. resnet50, efficientnet_b0
+            model = torchvision.models.__dict__[opt.model](
+                weights="IMAGENET1K_V1" if pretrained else None
+            )
+        else:
+            m = hub.list(
+                "ultralytics/yolov5"
+            )  # + hub.list('pytorch/vision')  # models
+            raise ModuleNotFoundError(
+                f"--model {opt.model} not found. Available models are: \n"
+                + "\n".join(m)
+            )
+        if isinstance(model, DetectionModel):
+            LOGGER.warning(
+                "WARNING ⚠️ pass YOLOv5 classifier model with '-cls' suffix, i.e. '--model yolov5s-cls.pt'"
+            )
+            model = ClassificationModel(
+                model=model, nc=nc, cutoff=opt.cutoff or 10
+            )  # convert to classification model
+        reshape_classifier_output(model, nc)  # update class count
+    for m in model.modules():
+        if not pretrained and hasattr(m, "reset_parameters"):
+            m.reset_parameters()
+        if isinstance(m, torch.nn.Dropout) and opt.dropout is not None:
+            m.p = opt.dropout  # set dropout
+    for p in model.parameters():
+        p.requires_grad = True  # for training
+    model = model.to(device)
+
+    # Info
+    if RANK in {-1, 0}:
+        model.names = trainloader.dataset.classes  # attach class names
+        model.transforms = (
+            testloader.dataset.torch_transforms
+        )  # attach inference transforms
+        model_info(model)
+        if opt.verbose:
+            LOGGER.info(model)
+        images, labels = next(iter(trainloader))
+        file = imshow_cls(
+            images[:25],
+            labels[:25],
+            names=model.names,
+            f=save_dir / "train_images.jpg",
+        )
+        logger.log_images(file, name="Train Examples")
+        logger.log_graph(model, imgsz)  # log model
+
+    # Optimizer
+    optimizer = smart_optimizer(
+        model, opt.optimizer, opt.lr0, momentum=0.9, decay=opt.decay
+    )
+
+    # Scheduler
+    lrf = 0.01  # final lr (fraction of lr0)
+    # lf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - lrf) + lrf  # cosine
+    lf = lambda x: (1 - x / epochs) * (1 - lrf) + lrf  # linear
+    scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
+    # scheduler = lr_scheduler.OneCycleLR(optimizer, max_lr=lr0, total_steps=epochs, pct_start=0.1,
+    #                                    final_div_factor=1 / 25 / lrf)
+
+    # EMA
+    ema = ModelEMA(model) if RANK in {-1, 0} else None
+
+    # DDP mode
+    if cuda and RANK != -1:
+        model = smart_DDP(model)
+
+    # Train
+    t0 = time.time()
+    criterion = smartCrossEntropyLoss(
+        label_smoothing=opt.label_smoothing
+    )  # loss function
+    best_fitness = 0.0
+    scaler = amp.GradScaler(enabled=cuda)
+    val = test_dir.stem  # 'val' or 'test'
+    LOGGER.info(
+        f"Image sizes {imgsz} train, {imgsz} test\n"
+        f"Using {nw * WORLD_SIZE} dataloader workers\n"
+        f"Logging results to {colorstr('bold', save_dir)}\n"
+        f"Starting {opt.model} training on {data} dataset with {nc} classes for {epochs} epochs...\n\n"
+        f"{'Epoch':>10}{'GPU_mem':>10}{'train_loss':>12}{f'{val}_loss':>12}{'top1_acc':>12}{'top5_acc':>12}"
+    )
+    for epoch in range(epochs):  # loop over the dataset multiple times
+        tloss, vloss, fitness = 0.0, 0.0, 0.0  # train loss, val loss, fitness
+        model.train()
+        if RANK != -1:
+            trainloader.sampler.set_epoch(epoch)
+        pbar = enumerate(trainloader)
+        if RANK in {-1, 0}:
+            pbar = tqdm(
+                enumerate(trainloader),
+                total=len(trainloader),
+                bar_format=TQDM_BAR_FORMAT,
+            )
+        for i, (images, labels) in pbar:  # progress bar
+            images, labels = images.to(device, non_blocking=True), labels.to(
+                device
+            )
+
+            # Forward
+            with amp.autocast(enabled=cuda):  # stability issues when enabled
+                loss = criterion(model(images), labels)
+
+            # Backward
+            scaler.scale(loss).backward()
+
+            # Optimize
+            scaler.unscale_(optimizer)  # unscale gradients
+            torch.nn.utils.clip_grad_norm_(
+                model.parameters(), max_norm=10.0
+            )  # clip gradients
+            scaler.step(optimizer)
+            scaler.update()
+            optimizer.zero_grad()
+            if ema:
+                ema.update(model)
+
+            if RANK in {-1, 0}:
+                # Print
+                tloss = (tloss * i + loss.item()) / (
+                    i + 1
+                )  # update mean losses
+                mem = "%.3gG" % (
+                    torch.cuda.memory_reserved() / 1e9
+                    if torch.cuda.is_available()
+                    else 0
+                )  # (GB)
+                pbar.desc = (
+                    f"{f'{epoch + 1}/{epochs}':>10}{mem:>10}{tloss:>12.3g}"
+                    + " " * 36
+                )
+
+                # Test
+                if i == len(pbar) - 1:  # last batch
+                    top1, top5, vloss = validate.run(
+                        model=ema.ema,
+                        dataloader=testloader,
+                        criterion=criterion,
+                        pbar=pbar,
+                    )  # test accuracy, loss
+                    fitness = top1  # define fitness as top1 accuracy
+
+        # Scheduler
+        scheduler.step()
+
+        # Log metrics
+        if RANK in {-1, 0}:
+            # Best fitness
+            if fitness > best_fitness:
+                best_fitness = fitness
+
+            # Log
+            metrics = {
+                "train/loss": tloss,
+                f"{val}/loss": vloss,
+                "metrics/accuracy_top1": top1,
+                "metrics/accuracy_top5": top5,
+                "lr/0": optimizer.param_groups[0]["lr"],
+            }  # learning rate
+            logger.log_metrics(metrics, epoch)
+
+            # Save model
+            final_epoch = epoch + 1 == epochs
+            if (not opt.nosave) or final_epoch:
+                ckpt = {
+                    "epoch": epoch,
+                    "best_fitness": best_fitness,
+                    "model": deepcopy(
+                        ema.ema
+                    ).half(),  # deepcopy(de_parallel(model)).half(),
+                    "ema": None,  # deepcopy(ema.ema).half(),
+                    "updates": ema.updates,
+                    "optimizer": None,  # optimizer.state_dict(),
+                    "opt": vars(opt),
+                    "git": GIT_INFO,  # {remote, branch, commit} if a git repo
+                    "date": datetime.now().isoformat(),
+                }
+
+                # Save last, best and delete
+                torch.save(ckpt, last)
+                if best_fitness == fitness:
+                    torch.save(ckpt, best)
+                del ckpt
+
+    # Train complete
+    if RANK in {-1, 0} and final_epoch:
+        LOGGER.info(
+            f"\nTraining complete ({(time.time() - t0) / 3600:.3f} hours)"
+            f"\nResults saved to {colorstr('bold', save_dir)}"
+            f"\nPredict:         python classify/predict.py --weights {best} --source im.jpg"
+            f"\nValidate:        python classify/val.py --weights {best} --data {data_dir}"
+            f"\nExport:          python export.py --weights {best} --include onnx"
+            f"\nPyTorch Hub:     model = torch.hub.load('ultralytics/yolov5', 'custom', '{best}')"
+            f"\nVisualize:       https://netron.app\n"
+        )
+
+        # Plot examples
+        images, labels = (
+            x[:25] for x in next(iter(testloader))
+        )  # first 25 images and labels
+        pred = torch.max(ema.ema(images.to(device)), 1)[1]
+        file = imshow_cls(
+            images,
+            labels,
+            pred,
+            model.names,
+            verbose=False,
+            f=save_dir / "test_images.jpg",
+        )
+
+        # Log results
+        meta = {
+            "epochs": epochs,
+            "top1_acc": best_fitness,
+            "date": datetime.now().isoformat(),
+        }
+        logger.log_images(
+            file, name="Test Examples (true-predicted)", epoch=epoch
+        )
+        logger.log_model(best, epochs, metadata=meta)
+
+
+def parse_opt(known=False):
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model",
+        type=str,
+        default="yolov5s-cls.pt",
+        help="initial weights path",
+    )
+    parser.add_argument(
+        "--data",
+        type=str,
+        default="imagenette160",
+        help="cifar10, cifar100, mnist, imagenet, ...",
+    )
+    parser.add_argument(
+        "--epochs", type=int, default=10, help="total training epochs"
+    )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=64,
+        help="total batch size for all GPUs",
+    )
+    parser.add_argument(
+        "--imgsz",
+        "--img",
+        "--img-size",
+        type=int,
+        default=224,
+        help="train, val image size (pixels)",
+    )
+    parser.add_argument(
+        "--nosave", action="store_true", help="only save final checkpoint"
+    )
+    parser.add_argument(
+        "--cache",
+        type=str,
+        nargs="?",
+        const="ram",
+        help='--cache images in "ram" (default) or "disk"',
+    )
+    parser.add_argument(
+        "--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu"
+    )
+    parser.add_argument(
+        "--workers",
+        type=int,
+        default=8,
+        help="max dataloader workers (per RANK in DDP mode)",
+    )
+    parser.add_argument(
+        "--project",
+        default=ROOT / "runs/train-cls",
+        help="save to project/name",
+    )
+    parser.add_argument("--name", default="exp", help="save to project/name")
+    parser.add_argument(
+        "--exist-ok",
+        action="store_true",
+        help="existing project/name ok, do not increment",
+    )
+    parser.add_argument(
+        "--pretrained",
+        nargs="?",
+        const=True,
+        default=True,
+        help="start from i.e. --pretrained False",
+    )
+    parser.add_argument(
+        "--optimizer",
+        choices=["SGD", "Adam", "AdamW", "RMSProp"],
+        default="Adam",
+        help="optimizer",
+    )
+    parser.add_argument(
+        "--lr0", type=float, default=0.001, help="initial learning rate"
+    )
+    parser.add_argument(
+        "--decay", type=float, default=5e-5, help="weight decay"
+    )
+    parser.add_argument(
+        "--label-smoothing",
+        type=float,
+        default=0.1,
+        help="Label smoothing epsilon",
+    )
+    parser.add_argument(
+        "--cutoff",
+        type=int,
+        default=None,
+        help="Model layer cutoff index for Classify() head",
+    )
+    parser.add_argument(
+        "--dropout", type=float, default=None, help="Dropout (fraction)"
+    )
+    parser.add_argument("--verbose", action="store_true", help="Verbose mode")
+    parser.add_argument(
+        "--seed", type=int, default=0, help="Global training seed"
+    )
+    parser.add_argument(
+        "--local_rank",
+        type=int,
+        default=-1,
+        help="Automatic DDP Multi-GPU argument, do not modify",
+    )
+    return parser.parse_known_args()[0] if known else parser.parse_args()
+
+
+def main(opt):
+    # Checks
+    if RANK in {-1, 0}:
+        print_args(vars(opt))
+        check_git_status()
+        check_requirements()
+
+    # DDP mode
+    device = select_device(opt.device, batch_size=opt.batch_size)
+    if LOCAL_RANK != -1:
+        assert (
+            opt.batch_size != -1
+        ), "AutoBatch is coming soon for classification, please pass a valid --batch-size"
+        assert (
+            opt.batch_size % WORLD_SIZE == 0
+        ), f"--batch-size {opt.batch_size} must be multiple of WORLD_SIZE"
+        assert (
+            torch.cuda.device_count() > LOCAL_RANK
+        ), "insufficient CUDA devices for DDP command"
+        torch.cuda.set_device(LOCAL_RANK)
+        device = torch.device("cuda", LOCAL_RANK)
+        dist.init_process_group(
+            backend="nccl" if dist.is_nccl_available() else "gloo"
+        )
+
+    # Parameters
+    opt.save_dir = increment_path(
+        Path(opt.project) / opt.name, exist_ok=opt.exist_ok
+    )  # increment run
+
+    # Train
+    train(opt, device)
+
+
+def run(**kwargs):
+    # Usage: from yolov5 import classify; classify.train.run(data=mnist, imgsz=320, model='yolov5m')
+    opt = parse_opt(True)
+    for k, v in kwargs.items():
+        setattr(opt, k, v)
+    main(opt)
+    return opt
+
+
+if __name__ == "__main__":
+    opt = parse_opt()
+    main(opt)

classify/val.py

@@ -0,0 +1,259 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Validate a trained YOLOv5 classification model on a classification dataset
+
+Usage:
+    $ bash data/scripts/get_imagenet.sh --val  # download ImageNet val split (6.3G, 50000 images)
+    $ python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224  # validate ImageNet
+
+Usage - formats:
+    $ python classify/val.py --weights yolov5s-cls.pt                 # PyTorch
+                                       yolov5s-cls.torchscript        # TorchScript
+                                       yolov5s-cls.onnx               # ONNX Runtime or OpenCV DNN with --dnn
+                                       yolov5s-cls_openvino_model     # OpenVINO
+                                       yolov5s-cls.engine             # TensorRT
+                                       yolov5s-cls.mlmodel            # CoreML (macOS-only)
+                                       yolov5s-cls_saved_model        # TensorFlow SavedModel
+                                       yolov5s-cls.pb                 # TensorFlow GraphDef
+                                       yolov5s-cls.tflite             # TensorFlow Lite
+                                       yolov5s-cls_edgetpu.tflite     # TensorFlow Edge TPU
+                                       yolov5s-cls_paddle_model       # PaddlePaddle
+"""
+
+import argparse
+import os
+import sys
+from pathlib import Path
+
+import torch
+from tqdm import tqdm
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[1]  # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
+
+from models.common import DetectMultiBackend
+from utils.dataloaders import create_classification_dataloader
+from utils.general import (
+    LOGGER,
+    TQDM_BAR_FORMAT,
+    Profile,
+    check_img_size,
+    check_requirements,
+    colorstr,
+    increment_path,
+    print_args,
+)
+from utils.torch_utils import select_device, smart_inference_mode
+
+
+@smart_inference_mode()
+def run(
+    data=ROOT / "../datasets/mnist",  # dataset dir
+    weights=ROOT / "yolov5s-cls.pt",  # model.pt path(s)
+    batch_size=128,  # batch size
+    imgsz=224,  # inference size (pixels)
+    device="",  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+    workers=8,  # max dataloader workers (per RANK in DDP mode)
+    verbose=False,  # verbose output
+    project=ROOT / "runs/val-cls",  # save to project/name
+    name="exp",  # save to project/name
+    exist_ok=False,  # existing project/name ok, do not increment
+    half=False,  # use FP16 half-precision inference
+    dnn=False,  # use OpenCV DNN for ONNX inference
+    model=None,
+    dataloader=None,
+    criterion=None,
+    pbar=None,
+):
+    # Initialize/load model and set device
+    training = model is not None
+    if training:  # called by train.py
+        device, pt, jit, engine = (
+            next(model.parameters()).device,
+            True,
+            False,
+            False,
+        )  # get model device, PyTorch model
+        half &= device.type != "cpu"  # half precision only supported on CUDA
+        model.half() if half else model.float()
+    else:  # called directly
+        device = select_device(device, batch_size=batch_size)
+
+        # Directories
+        save_dir = increment_path(
+            Path(project) / name, exist_ok=exist_ok
+        )  # increment run
+        save_dir.mkdir(parents=True, exist_ok=True)  # make dir
+
+        # Load model
+        model = DetectMultiBackend(weights, device=device, dnn=dnn, fp16=half)
+        stride, pt, jit, engine = (
+            model.stride,
+            model.pt,
+            model.jit,
+            model.engine,
+        )
+        imgsz = check_img_size(imgsz, s=stride)  # check image size
+        half = model.fp16  # FP16 supported on limited backends with CUDA
+        if engine:
+            batch_size = model.batch_size
+        else:
+            device = model.device
+            if not (pt or jit):
+                batch_size = 1  # export.py models default to batch-size 1
+                LOGGER.info(
+                    f"Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models"
+                )
+
+        # Dataloader
+        data = Path(data)
+        test_dir = (
+            data / "test" if (data / "test").exists() else data / "val"
+        )  # data/test or data/val
+        dataloader = create_classification_dataloader(
+            path=test_dir,
+            imgsz=imgsz,
+            batch_size=batch_size,
+            augment=False,
+            rank=-1,
+            workers=workers,
+        )
+
+    model.eval()
+    pred, targets, loss, dt = [], [], 0, (Profile(), Profile(), Profile())
+    n = len(dataloader)  # number of batches
+    action = (
+        "validating" if dataloader.dataset.root.stem == "val" else "testing"
+    )
+    desc = f"{pbar.desc[:-36]}{action:>36}" if pbar else f"{action}"
+    bar = tqdm(
+        dataloader,
+        desc,
+        n,
+        not training,
+        bar_format=TQDM_BAR_FORMAT,
+        position=0,
+    )
+    with torch.cuda.amp.autocast(enabled=device.type != "cpu"):
+        for images, labels in bar:
+            with dt[0]:
+                images, labels = images.to(
+                    device, non_blocking=True
+                ), labels.to(device)
+
+            with dt[1]:
+                y = model(images)
+
+            with dt[2]:
+                pred.append(y.argsort(1, descending=True)[:, :5])
+                targets.append(labels)
+                if criterion:
+                    loss += criterion(y, labels)
+
+    loss /= n
+    pred, targets = torch.cat(pred), torch.cat(targets)
+    correct = (targets[:, None] == pred).float()
+    acc = torch.stack(
+        (correct[:, 0], correct.max(1).values), dim=1
+    )  # (top1, top5) accuracy
+    top1, top5 = acc.mean(0).tolist()
+
+    if pbar:
+        pbar.desc = f"{pbar.desc[:-36]}{loss:>12.3g}{top1:>12.3g}{top5:>12.3g}"
+    if verbose:  # all classes
+        LOGGER.info(
+            f"{'Class':>24}{'Images':>12}{'top1_acc':>12}{'top5_acc':>12}"
+        )
+        LOGGER.info(
+            f"{'all':>24}{targets.shape[0]:>12}{top1:>12.3g}{top5:>12.3g}"
+        )
+        for i, c in model.names.items():
+            aci = acc[targets == i]
+            top1i, top5i = aci.mean(0).tolist()
+            LOGGER.info(
+                f"{c:>24}{aci.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}"
+            )
+
+        # Print results
+        t = tuple(
+            x.t / len(dataloader.dataset.samples) * 1e3 for x in dt
+        )  # speeds per image
+        shape = (1, 3, imgsz, imgsz)
+        LOGGER.info(
+            f"Speed: %.1fms pre-process, %.1fms inference, %.1fms post-process per image at shape {shape}"
+            % t
+        )
+        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")
+
+    return top1, top5, loss
+
+
+def parse_opt():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--data",
+        type=str,
+        default=ROOT / "../datasets/mnist",
+        help="dataset path",
+    )
+    parser.add_argument(
+        "--weights",
+        nargs="+",
+        type=str,
+        default=ROOT / "yolov5s-cls.pt",
+        help="model.pt path(s)",
+    )
+    parser.add_argument(
+        "--batch-size", type=int, default=128, help="batch size"
+    )
+    parser.add_argument(
+        "--imgsz",
+        "--img",
+        "--img-size",
+        type=int,
+        default=224,
+        help="inference size (pixels)",
+    )
+    parser.add_argument(
+        "--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu"
+    )
+    parser.add_argument(
+        "--workers",
+        type=int,
+        default=8,
+        help="max dataloader workers (per RANK in DDP mode)",
+    )
+    parser.add_argument(
+        "--verbose", nargs="?", const=True, default=True, help="verbose output"
+    )
+    parser.add_argument(
+        "--project", default=ROOT / "runs/val-cls", help="save to project/name"
+    )
+    parser.add_argument("--name", default="exp", help="save to project/name")
+    parser.add_argument(
+        "--exist-ok",
+        action="store_true",
+        help="existing project/name ok, do not increment",
+    )
+    parser.add_argument(
+        "--half", action="store_true", help="use FP16 half-precision inference"
+    )
+    parser.add_argument(
+        "--dnn", action="store_true", help="use OpenCV DNN for ONNX inference"
+    )
+    opt = parser.parse_args()
+    print_args(vars(opt))
+    return opt
+
+
+def main(opt):
+    check_requirements(exclude=("tensorboard", "thop"))
+    run(**vars(opt))
+
+
+if __name__ == "__main__":
+    opt = parse_opt()
+    main(opt)

data/Argoverse.yaml

@@ -0,0 +1,74 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
+# Example usage: python train.py --data Argoverse.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── Argoverse  ← downloads here (31.3 GB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/Argoverse  # dataset root dir
+train: Argoverse-1.1/images/train/  # train images (relative to 'path') 39384 images
+val: Argoverse-1.1/images/val/  # val images (relative to 'path') 15062 images
+test: Argoverse-1.1/images/test/  # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: bus
+  5: truck
+  6: traffic_light
+  7: stop_sign
+
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  import json
+
+  from tqdm import tqdm
+  from utils.general import download, Path
+
+
+  def argoverse2yolo(set):
+      labels = {}
+      a = json.load(open(set, "rb"))
+      for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."):
+          img_id = annot['image_id']
+          img_name = a['images'][img_id]['name']
+          img_label_name = f'{img_name[:-3]}txt'
+
+          cls = annot['category_id']  # instance class id
+          x_center, y_center, width, height = annot['bbox']
+          x_center = (x_center + width / 2) / 1920.0  # offset and scale
+          y_center = (y_center + height / 2) / 1200.0  # offset and scale
+          width /= 1920.0  # scale
+          height /= 1200.0  # scale
+
+          img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']]
+          if not img_dir.exists():
+              img_dir.mkdir(parents=True, exist_ok=True)
+
+          k = str(img_dir / img_label_name)
+          if k not in labels:
+              labels[k] = []
+          labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n")
+
+      for k in labels:
+          with open(k, "w") as f:
+              f.writelines(labels[k])
+
+
+  # Download
+  dir = Path(yaml['path'])  # dataset root dir
+  urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip']
+  download(urls, dir=dir, delete=False)
+
+  # Convert
+  annotations_dir = 'Argoverse-HD/annotations/'
+  (dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images')  # rename 'tracking' to 'images'
+  for d in "train.json", "val.json":
+      argoverse2yolo(dir / annotations_dir / d)  # convert VisDrone annotations to YOLO labels

data/GlobalWheat2020.yaml

@@ -0,0 +1,54 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan
+# Example usage: python train.py --data GlobalWheat2020.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── GlobalWheat2020  ← downloads here (7.0 GB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/GlobalWheat2020  # dataset root dir
+train: # train images (relative to 'path') 3422 images
+  - images/arvalis_1
+  - images/arvalis_2
+  - images/arvalis_3
+  - images/ethz_1
+  - images/rres_1
+  - images/inrae_1
+  - images/usask_1
+val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1)
+  - images/ethz_1
+test: # test images (optional) 1276 images
+  - images/utokyo_1
+  - images/utokyo_2
+  - images/nau_1
+  - images/uq_1
+
+# Classes
+names:
+  0: wheat_head
+
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  from utils.general import download, Path
+
+
+  # Download
+  dir = Path(yaml['path'])  # dataset root dir
+  urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
+          'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip']
+  download(urls, dir=dir)
+
+  # Make Directories
+  for p in 'annotations', 'images', 'labels':
+      (dir / p).mkdir(parents=True, exist_ok=True)
+
+  # Move
+  for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
+           'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
+      (dir / p).rename(dir / 'images' / p)  # move to /images
+      f = (dir / p).with_suffix('.json')  # json file
+      if f.exists():
+          f.rename((dir / 'annotations' / p).with_suffix('.json'))  # move to /annotations

data/ImageNet.yaml

@@ -0,0 +1,1022 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
+# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
+# Example usage: python classify/train.py --data imagenet
+# parent
+# ├── yolov5
+# └── datasets
+#     └── imagenet  ← downloads here (144 GB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/imagenet  # dataset root dir
+train: train  # train images (relative to 'path') 1281167 images
+val: val  # val images (relative to 'path') 50000 images
+test:  # test images (optional)
+
+# Classes
+names:
+  0: tench
+  1: goldfish
+  2: great white shark
+  3: tiger shark
+  4: hammerhead shark
+  5: electric ray
+  6: stingray
+  7: cock
+  8: hen
+  9: ostrich
+  10: brambling
+  11: goldfinch
+  12: house finch
+  13: junco
+  14: indigo bunting
+  15: American robin
+  16: bulbul
+  17: jay
+  18: magpie
+  19: chickadee
+  20: American dipper
+  21: kite
+  22: bald eagle
+  23: vulture
+  24: great grey owl
+  25: fire salamander
+  26: smooth newt
+  27: newt
+  28: spotted salamander
+  29: axolotl
+  30: American bullfrog
+  31: tree frog
+  32: tailed frog
+  33: loggerhead sea turtle
+  34: leatherback sea turtle
+  35: mud turtle
+  36: terrapin
+  37: box turtle
+  38: banded gecko
+  39: green iguana
+  40: Carolina anole
+  41: desert grassland whiptail lizard
+  42: agama
+  43: frilled-necked lizard
+  44: alligator lizard
+  45: Gila monster
+  46: European green lizard
+  47: chameleon
+  48: Komodo dragon
+  49: Nile crocodile
+  50: American alligator
+  51: triceratops
+  52: worm snake
+  53: ring-necked snake
+  54: eastern hog-nosed snake
+  55: smooth green snake
+  56: kingsnake
+  57: garter snake
+  58: water snake
+  59: vine snake
+  60: night snake
+  61: boa constrictor
+  62: African rock python
+  63: Indian cobra
+  64: green mamba
+  65: sea snake
+  66: Saharan horned viper
+  67: eastern diamondback rattlesnake
+  68: sidewinder
+  69: trilobite
+  70: harvestman
+  71: scorpion
+  72: yellow garden spider
+  73: barn spider
+  74: European garden spider
+  75: southern black widow
+  76: tarantula
+  77: wolf spider
+  78: tick
+  79: centipede
+  80: black grouse
+  81: ptarmigan
+  82: ruffed grouse
+  83: prairie grouse
+  84: peacock
+  85: quail
+  86: partridge
+  87: grey parrot
+  88: macaw
+  89: sulphur-crested cockatoo
+  90: lorikeet
+  91: coucal
+  92: bee eater
+  93: hornbill
+  94: hummingbird
+  95: jacamar
+  96: toucan
+  97: duck
+  98: red-breasted merganser
+  99: goose
+  100: black swan
+  101: tusker
+  102: echidna
+  103: platypus
+  104: wallaby
+  105: koala
+  106: wombat
+  107: jellyfish
+  108: sea anemone
+  109: brain coral
+  110: flatworm
+  111: nematode
+  112: conch
+  113: snail
+  114: slug
+  115: sea slug
+  116: chiton
+  117: chambered nautilus
+  118: Dungeness crab
+  119: rock crab
+  120: fiddler crab
+  121: red king crab
+  122: American lobster
+  123: spiny lobster
+  124: crayfish
+  125: hermit crab
+  126: isopod
+  127: white stork
+  128: black stork
+  129: spoonbill
+  130: flamingo
+  131: little blue heron
+  132: great egret
+  133: bittern
+  134: crane (bird)
+  135: limpkin
+  136: common gallinule
+  137: American coot
+  138: bustard
+  139: ruddy turnstone
+  140: dunlin
+  141: common redshank
+  142: dowitcher
+  143: oystercatcher
+  144: pelican
+  145: king penguin
+  146: albatross
+  147: grey whale
+  148: killer whale
+  149: dugong
+  150: sea lion
+  151: Chihuahua
+  152: Japanese Chin
+  153: Maltese
+  154: Pekingese
+  155: Shih Tzu
+  156: King Charles Spaniel
+  157: Papillon
+  158: toy terrier
+  159: Rhodesian Ridgeback
+  160: Afghan Hound
+  161: Basset Hound
+  162: Beagle
+  163: Bloodhound
+  164: Bluetick Coonhound
+  165: Black and Tan Coonhound
+  166: Treeing Walker Coonhound
+  167: English foxhound
+  168: Redbone Coonhound
+  169: borzoi
+  170: Irish Wolfhound
+  171: Italian Greyhound
+  172: Whippet
+  173: Ibizan Hound
+  174: Norwegian Elkhound
+  175: Otterhound
+  176: Saluki
+  177: Scottish Deerhound
+  178: Weimaraner
+  179: Staffordshire Bull Terrier
+  180: American Staffordshire Terrier
+  181: Bedlington Terrier
+  182: Border Terrier
+  183: Kerry Blue Terrier
+  184: Irish Terrier
+  185: Norfolk Terrier
+  186: Norwich Terrier
+  187: Yorkshire Terrier
+  188: Wire Fox Terrier
+  189: Lakeland Terrier
+  190: Sealyham Terrier
+  191: Airedale Terrier
+  192: Cairn Terrier
+  193: Australian Terrier
+  194: Dandie Dinmont Terrier
+  195: Boston Terrier
+  196: Miniature Schnauzer
+  197: Giant Schnauzer
+  198: Standard Schnauzer
+  199: Scottish Terrier
+  200: Tibetan Terrier
+  201: Australian Silky Terrier
+  202: Soft-coated Wheaten Terrier
+  203: West Highland White Terrier
+  204: Lhasa Apso
+  205: Flat-Coated Retriever
+  206: Curly-coated Retriever
+  207: Golden Retriever
+  208: Labrador Retriever
+  209: Chesapeake Bay Retriever
+  210: German Shorthaired Pointer
+  211: Vizsla
+  212: English Setter
+  213: Irish Setter
+  214: Gordon Setter
+  215: Brittany
+  216: Clumber Spaniel
+  217: English Springer Spaniel
+  218: Welsh Springer Spaniel
+  219: Cocker Spaniels
+  220: Sussex Spaniel
+  221: Irish Water Spaniel
+  222: Kuvasz
+  223: Schipperke
+  224: Groenendael
+  225: Malinois
+  226: Briard
+  227: Australian Kelpie
+  228: Komondor
+  229: Old English Sheepdog
+  230: Shetland Sheepdog
+  231: collie
+  232: Border Collie
+  233: Bouvier des Flandres
+  234: Rottweiler
+  235: German Shepherd Dog
+  236: Dobermann
+  237: Miniature Pinscher
+  238: Greater Swiss Mountain Dog
+  239: Bernese Mountain Dog
+  240: Appenzeller Sennenhund
+  241: Entlebucher Sennenhund
+  242: Boxer
+  243: Bullmastiff
+  244: Tibetan Mastiff
+  245: French Bulldog
+  246: Great Dane
+  247: St. Bernard
+  248: husky
+  249: Alaskan Malamute
+  250: Siberian Husky
+  251: Dalmatian
+  252: Affenpinscher
+  253: Basenji
+  254: pug
+  255: Leonberger
+  256: Newfoundland
+  257: Pyrenean Mountain Dog
+  258: Samoyed
+  259: Pomeranian
+  260: Chow Chow
+  261: Keeshond
+  262: Griffon Bruxellois
+  263: Pembroke Welsh Corgi
+  264: Cardigan Welsh Corgi
+  265: Toy Poodle
+  266: Miniature Poodle
+  267: Standard Poodle
+  268: Mexican hairless dog
+  269: grey wolf
+  270: Alaskan tundra wolf
+  271: red wolf
+  272: coyote
+  273: dingo
+  274: dhole
+  275: African wild dog
+  276: hyena
+  277: red fox
+  278: kit fox
+  279: Arctic fox
+  280: grey fox
+  281: tabby cat
+  282: tiger cat
+  283: Persian cat
+  284: Siamese cat
+  285: Egyptian Mau
+  286: cougar
+  287: lynx
+  288: leopard
+  289: snow leopard
+  290: jaguar
+  291: lion
+  292: tiger
+  293: cheetah
+  294: brown bear
+  295: American black bear
+  296: polar bear
+  297: sloth bear
+  298: mongoose
+  299: meerkat
+  300: tiger beetle
+  301: ladybug
+  302: ground beetle
+  303: longhorn beetle
+  304: leaf beetle
+  305: dung beetle
+  306: rhinoceros beetle
+  307: weevil
+  308: fly
+  309: bee
+  310: ant
+  311: grasshopper
+  312: cricket
+  313: stick insect
+  314: cockroach
+  315: mantis
+  316: cicada
+  317: leafhopper
+  318: lacewing
+  319: dragonfly
+  320: damselfly
+  321: red admiral
+  322: ringlet
+  323: monarch butterfly
+  324: small white
+  325: sulphur butterfly
+  326: gossamer-winged butterfly
+  327: starfish
+  328: sea urchin
+  329: sea cucumber
+  330: cottontail rabbit
+  331: hare
+  332: Angora rabbit
+  333: hamster
+  334: porcupine
+  335: fox squirrel
+  336: marmot
+  337: beaver
+  338: guinea pig
+  339: common sorrel
+  340: zebra
+  341: pig
+  342: wild boar
+  343: warthog
+  344: hippopotamus
+  345: ox
+  346: water buffalo
+  347: bison
+  348: ram
+  349: bighorn sheep
+  350: Alpine ibex
+  351: hartebeest
+  352: impala
+  353: gazelle
+  354: dromedary
+  355: llama
+  356: weasel
+  357: mink
+  358: European polecat
+  359: black-footed ferret
+  360: otter
+  361: skunk
+  362: badger
+  363: armadillo
+  364: three-toed sloth
+  365: orangutan
+  366: gorilla
+  367: chimpanzee
+  368: gibbon
+  369: siamang
+  370: guenon
+  371: patas monkey
+  372: baboon
+  373: macaque
+  374: langur
+  375: black-and-white colobus
+  376: proboscis monkey
+  377: marmoset
+  378: white-headed capuchin
+  379: howler monkey
+  380: titi
+  381: Geoffroy's spider monkey
+  382: common squirrel monkey
+  383: ring-tailed lemur
+  384: indri
+  385: Asian elephant
+  386: African bush elephant
+  387: red panda
+  388: giant panda
+  389: snoek
+  390: eel
+  391: coho salmon
+  392: rock beauty
+  393: clownfish
+  394: sturgeon
+  395: garfish
+  396: lionfish
+  397: pufferfish
+  398: abacus
+  399: abaya
+  400: academic gown
+  401: accordion
+  402: acoustic guitar
+  403: aircraft carrier
+  404: airliner
+  405: airship
+  406: altar
+  407: ambulance
+  408: amphibious vehicle
+  409: analog clock
+  410: apiary
+  411: apron
+  412: waste container
+  413: assault rifle
+  414: backpack
+  415: bakery
+  416: balance beam
+  417: balloon
+  418: ballpoint pen
+  419: Band-Aid
+  420: banjo
+  421: baluster
+  422: barbell
+  423: barber chair
+  424: barbershop
+  425: barn
+  426: barometer
+  427: barrel
+  428: wheelbarrow
+  429: baseball
+  430: basketball
+  431: bassinet
+  432: bassoon
+  433: swimming cap
+  434: bath towel
+  435: bathtub
+  436: station wagon
+  437: lighthouse
+  438: beaker
+  439: military cap
+  440: beer bottle
+  441: beer glass
+  442: bell-cot
+  443: bib
+  444: tandem bicycle
+  445: bikini
+  446: ring binder
+  447: binoculars
+  448: birdhouse
+  449: boathouse
+  450: bobsleigh
+  451: bolo tie
+  452: poke bonnet
+  453: bookcase
+  454: bookstore
+  455: bottle cap
+  456: bow
+  457: bow tie
+  458: brass
+  459: bra
+  460: breakwater
+  461: breastplate
+  462: broom
+  463: bucket
+  464: buckle
+  465: bulletproof vest
+  466: high-speed train
+  467: butcher shop
+  468: taxicab
+  469: cauldron
+  470: candle
+  471: cannon
+  472: canoe
+  473: can opener
+  474: cardigan
+  475: car mirror
+  476: carousel
+  477: tool kit
+  478: carton
+  479: car wheel
+  480: automated teller machine
+  481: cassette
+  482: cassette player
+  483: castle
+  484: catamaran
+  485: CD player
+  486: cello
+  487: mobile phone
+  488: chain
+  489: chain-link fence
+  490: chain mail
+  491: chainsaw
+  492: chest
+  493: chiffonier
+  494: chime
+  495: china cabinet
+  496: Christmas stocking
+  497: church
+  498: movie theater
+  499: cleaver
+  500: cliff dwelling
+  501: cloak
+  502: clogs
+  503: cocktail shaker
+  504: coffee mug
+  505: coffeemaker
+  506: coil
+  507: combination lock
+  508: computer keyboard
+  509: confectionery store
+  510: container ship
+  511: convertible
+  512: corkscrew
+  513: cornet
+  514: cowboy boot
+  515: cowboy hat
+  516: cradle
+  517: crane (machine)
+  518: crash helmet
+  519: crate
+  520: infant bed
+  521: Crock Pot
+  522: croquet ball
+  523: crutch
+  524: cuirass
+  525: dam
+  526: desk
+  527: desktop computer
+  528: rotary dial telephone
+  529: diaper
+  530: digital clock
+  531: digital watch
+  532: dining table
+  533: dishcloth
+  534: dishwasher
+  535: disc brake
+  536: dock
+  537: dog sled
+  538: dome
+  539: doormat
+  540: drilling rig
+  541: drum
+  542: drumstick
+  543: dumbbell
+  544: Dutch oven
+  545: electric fan
+  546: electric guitar
+  547: electric locomotive
+  548: entertainment center
+  549: envelope
+  550: espresso machine
+  551: face powder
+  552: feather boa
+  553: filing cabinet
+  554: fireboat
+  555: fire engine
+  556: fire screen sheet
+  557: flagpole
+  558: flute
+  559: folding chair
+  560: football helmet
+  561: forklift
+  562: fountain
+  563: fountain pen
+  564: four-poster bed
+  565: freight car
+  566: French horn
+  567: frying pan
+  568: fur coat
+  569: garbage truck
+  570: gas mask
+  571: gas pump
+  572: goblet
+  573: go-kart
+  574: golf ball
+  575: golf cart
+  576: gondola
+  577: gong
+  578: gown
+  579: grand piano
+  580: greenhouse
+  581: grille
+  582: grocery store
+  583: guillotine
+  584: barrette
+  585: hair spray
+  586: half-track
+  587: hammer
+  588: hamper
+  589: hair dryer
+  590: hand-held computer
+  591: handkerchief
+  592: hard disk drive
+  593: harmonica
+  594: harp
+  595: harvester
+  596: hatchet
+  597: holster
+  598: home theater
+  599: honeycomb
+  600: hook
+  601: hoop skirt
+  602: horizontal bar
+  603: horse-drawn vehicle
+  604: hourglass
+  605: iPod
+  606: clothes iron
+  607: jack-o'-lantern
+  608: jeans
+  609: jeep
+  610: T-shirt
+  611: jigsaw puzzle
+  612: pulled rickshaw
+  613: joystick
+  614: kimono
+  615: knee pad
+  616: knot
+  617: lab coat
+  618: ladle
+  619: lampshade
+  620: laptop computer
+  621: lawn mower
+  622: lens cap
+  623: paper knife
+  624: library
+  625: lifeboat
+  626: lighter
+  627: limousine
+  628: ocean liner
+  629: lipstick
+  630: slip-on shoe
+  631: lotion
+  632: speaker
+  633: loupe
+  634: sawmill
+  635: magnetic compass
+  636: mail bag
+  637: mailbox
+  638: tights
+  639: tank suit
+  640: manhole cover
+  641: maraca
+  642: marimba
+  643: mask
+  644: match
+  645: maypole
+  646: maze
+  647: measuring cup
+  648: medicine chest
+  649: megalith
+  650: microphone
+  651: microwave oven
+  652: military uniform
+  653: milk can
+  654: minibus
+  655: miniskirt
+  656: minivan
+  657: missile
+  658: mitten
+  659: mixing bowl
+  660: mobile home
+  661: Model T
+  662: modem
+  663: monastery
+  664: monitor
+  665: moped
+  666: mortar
+  667: square academic cap
+  668: mosque
+  669: mosquito net
+  670: scooter
+  671: mountain bike
+  672: tent
+  673: computer mouse
+  674: mousetrap
+  675: moving van
+  676: muzzle
+  677: nail
+  678: neck brace
+  679: necklace
+  680: nipple
+  681: notebook computer
+  682: obelisk
+  683: oboe
+  684: ocarina
+  685: odometer
+  686: oil filter
+  687: organ
+  688: oscilloscope
+  689: overskirt
+  690: bullock cart
+  691: oxygen mask
+  692: packet
+  693: paddle
+  694: paddle wheel
+  695: padlock
+  696: paintbrush
+  697: pajamas
+  698: palace
+  699: pan flute
+  700: paper towel
+  701: parachute
+  702: parallel bars
+  703: park bench
+  704: parking meter
+  705: passenger car
+  706: patio
+  707: payphone
+  708: pedestal
+  709: pencil case
+  710: pencil sharpener
+  711: perfume
+  712: Petri dish
+  713: photocopier
+  714: plectrum
+  715: Pickelhaube
+  716: picket fence
+  717: pickup truck
+  718: pier
+  719: piggy bank
+  720: pill bottle
+  721: pillow
+  722: ping-pong ball
+  723: pinwheel
+  724: pirate ship
+  725: pitcher
+  726: hand plane
+  727: planetarium
+  728: plastic bag
+  729: plate rack
+  730: plow
+  731: plunger
+  732: Polaroid camera
+  733: pole
+  734: police van
+  735: poncho
+  736: billiard table
+  737: soda bottle
+  738: pot
+  739: potter's wheel
+  740: power drill
+  741: prayer rug
+  742: printer
+  743: prison
+  744: projectile
+  745: projector
+  746: hockey puck
+  747: punching bag
+  748: purse
+  749: quill
+  750: quilt
+  751: race car
+  752: racket
+  753: radiator
+  754: radio
+  755: radio telescope
+  756: rain barrel
+  757: recreational vehicle
+  758: reel
+  759: reflex camera
+  760: refrigerator
+  761: remote control
+  762: restaurant
+  763: revolver
+  764: rifle
+  765: rocking chair
+  766: rotisserie
+  767: eraser
+  768: rugby ball
+  769: ruler
+  770: running shoe
+  771: safe
+  772: safety pin
+  773: salt shaker
+  774: sandal
+  775: sarong
+  776: saxophone
+  777: scabbard
+  778: weighing scale
+  779: school bus
+  780: schooner
+  781: scoreboard
+  782: CRT screen
+  783: screw
+  784: screwdriver
+  785: seat belt
+  786: sewing machine
+  787: shield
+  788: shoe store
+  789: shoji
+  790: shopping basket
+  791: shopping cart
+  792: shovel
+  793: shower cap
+  794: shower curtain
+  795: ski
+  796: ski mask
+  797: sleeping bag
+  798: slide rule
+  799: sliding door
+  800: slot machine
+  801: snorkel
+  802: snowmobile
+  803: snowplow
+  804: soap dispenser
+  805: soccer ball
+  806: sock
+  807: solar thermal collector
+  808: sombrero
+  809: soup bowl
+  810: space bar
+  811: space heater
+  812: space shuttle
+  813: spatula
+  814: motorboat
+  815: spider web
+  816: spindle
+  817: sports car
+  818: spotlight
+  819: stage
+  820: steam locomotive
+  821: through arch bridge
+  822: steel drum
+  823: stethoscope
+  824: scarf
+  825: stone wall
+  826: stopwatch
+  827: stove
+  828: strainer
+  829: tram
+  830: stretcher
+  831: couch
+  832: stupa
+  833: submarine
+  834: suit
+  835: sundial
+  836: sunglass
+  837: sunglasses
+  838: sunscreen
+  839: suspension bridge
+  840: mop
+  841: sweatshirt
+  842: swimsuit
+  843: swing
+  844: switch
+  845: syringe
+  846: table lamp
+  847: tank
+  848: tape player
+  849: teapot
+  850: teddy bear
+  851: television
+  852: tennis ball
+  853: thatched roof
+  854: front curtain
+  855: thimble
+  856: threshing machine
+  857: throne
+  858: tile roof
+  859: toaster
+  860: tobacco shop
+  861: toilet seat
+  862: torch
+  863: totem pole
+  864: tow truck
+  865: toy store
+  866: tractor
+  867: semi-trailer truck
+  868: tray
+  869: trench coat
+  870: tricycle
+  871: trimaran
+  872: tripod
+  873: triumphal arch
+  874: trolleybus
+  875: trombone
+  876: tub
+  877: turnstile
+  878: typewriter keyboard
+  879: umbrella
+  880: unicycle
+  881: upright piano
+  882: vacuum cleaner
+  883: vase
+  884: vault
+  885: velvet
+  886: vending machine
+  887: vestment
+  888: viaduct
+  889: violin
+  890: volleyball
+  891: waffle iron
+  892: wall clock
+  893: wallet
+  894: wardrobe
+  895: military aircraft
+  896: sink
+  897: washing machine
+  898: water bottle
+  899: water jug
+  900: water tower
+  901: whiskey jug
+  902: whistle
+  903: wig
+  904: window screen
+  905: window shade
+  906: Windsor tie
+  907: wine bottle
+  908: wing
+  909: wok
+  910: wooden spoon
+  911: wool
+  912: split-rail fence
+  913: shipwreck
+  914: yawl
+  915: yurt
+  916: website
+  917: comic book
+  918: crossword
+  919: traffic sign
+  920: traffic light
+  921: dust jacket
+  922: menu
+  923: plate
+  924: guacamole
+  925: consomme
+  926: hot pot
+  927: trifle
+  928: ice cream
+  929: ice pop
+  930: baguette
+  931: bagel
+  932: pretzel
+  933: cheeseburger
+  934: hot dog
+  935: mashed potato
+  936: cabbage
+  937: broccoli
+  938: cauliflower
+  939: zucchini
+  940: spaghetti squash
+  941: acorn squash
+  942: butternut squash
+  943: cucumber
+  944: artichoke
+  945: bell pepper
+  946: cardoon
+  947: mushroom
+  948: Granny Smith
+  949: strawberry
+  950: orange
+  951: lemon
+  952: fig
+  953: pineapple
+  954: banana
+  955: jackfruit
+  956: custard apple
+  957: pomegranate
+  958: hay
+  959: carbonara
+  960: chocolate syrup
+  961: dough
+  962: meatloaf
+  963: pizza
+  964: pot pie
+  965: burrito
+  966: red wine
+  967: espresso
+  968: cup
+  969: eggnog
+  970: alp
+  971: bubble
+  972: cliff
+  973: coral reef
+  974: geyser
+  975: lakeshore
+  976: promontory
+  977: shoal
+  978: seashore
+  979: valley
+  980: volcano
+  981: baseball player
+  982: bridegroom
+  983: scuba diver
+  984: rapeseed
+  985: daisy
+  986: yellow lady's slipper
+  987: corn
+  988: acorn
+  989: rose hip
+  990: horse chestnut seed
+  991: coral fungus
+  992: agaric
+  993: gyromitra
+  994: stinkhorn mushroom
+  995: earth star
+  996: hen-of-the-woods
+  997: bolete
+  998: ear
+  999: toilet paper
+
+
+# Download script/URL (optional)
+download: data/scripts/get_imagenet.sh

data/Objects365.yaml

@@ -0,0 +1,438 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Objects365 dataset https://www.objects365.org/ by Megvii
+# Example usage: python train.py --data Objects365.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── Objects365  ← downloads here (712 GB = 367G data + 345G zips)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/Objects365  # dataset root dir
+train: images/train  # train images (relative to 'path') 1742289 images
+val: images/val # val images (relative to 'path') 80000 images
+test:  # test images (optional)
+
+# Classes
+names:
+  0: Person
+  1: Sneakers
+  2: Chair
+  3: Other Shoes
+  4: Hat
+  5: Car
+  6: Lamp
+  7: Glasses
+  8: Bottle
+  9: Desk
+  10: Cup
+  11: Street Lights
+  12: Cabinet/shelf
+  13: Handbag/Satchel
+  14: Bracelet
+  15: Plate
+  16: Picture/Frame
+  17: Helmet
+  18: Book
+  19: Gloves
+  20: Storage box
+  21: Boat
+  22: Leather Shoes
+  23: Flower
+  24: Bench
+  25: Potted Plant
+  26: Bowl/Basin
+  27: Flag
+  28: Pillow
+  29: Boots
+  30: Vase
+  31: Microphone
+  32: Necklace
+  33: Ring
+  34: SUV
+  35: Wine Glass
+  36: Belt
+  37: Monitor/TV
+  38: Backpack
+  39: Umbrella
+  40: Traffic Light
+  41: Speaker
+  42: Watch
+  43: Tie
+  44: Trash bin Can
+  45: Slippers
+  46: Bicycle
+  47: Stool
+  48: Barrel/bucket
+  49: Van
+  50: Couch
+  51: Sandals
+  52: Basket
+  53: Drum
+  54: Pen/Pencil
+  55: Bus
+  56: Wild Bird
+  57: High Heels
+  58: Motorcycle
+  59: Guitar
+  60: Carpet
+  61: Cell Phone
+  62: Bread
+  63: Camera
+  64: Canned
+  65: Truck
+  66: Traffic cone
+  67: Cymbal
+  68: Lifesaver
+  69: Towel
+  70: Stuffed Toy
+  71: Candle
+  72: Sailboat
+  73: Laptop
+  74: Awning
+  75: Bed
+  76: Faucet
+  77: Tent
+  78: Horse
+  79: Mirror
+  80: Power outlet
+  81: Sink
+  82: Apple
+  83: Air Conditioner
+  84: Knife
+  85: Hockey Stick
+  86: Paddle
+  87: Pickup Truck
+  88: Fork
+  89: Traffic Sign
+  90: Balloon
+  91: Tripod
+  92: Dog
+  93: Spoon
+  94: Clock
+  95: Pot
+  96: Cow
+  97: Cake
+  98: Dinning Table
+  99: Sheep
+  100: Hanger
+  101: Blackboard/Whiteboard
+  102: Napkin
+  103: Other Fish
+  104: Orange/Tangerine
+  105: Toiletry
+  106: Keyboard
+  107: Tomato
+  108: Lantern
+  109: Machinery Vehicle
+  110: Fan
+  111: Green Vegetables
+  112: Banana
+  113: Baseball Glove
+  114: Airplane
+  115: Mouse
+  116: Train
+  117: Pumpkin
+  118: Soccer
+  119: Skiboard
+  120: Luggage
+  121: Nightstand
+  122: Tea pot
+  123: Telephone
+  124: Trolley
+  125: Head Phone
+  126: Sports Car
+  127: Stop Sign
+  128: Dessert
+  129: Scooter
+  130: Stroller
+  131: Crane
+  132: Remote
+  133: Refrigerator
+  134: Oven
+  135: Lemon
+  136: Duck
+  137: Baseball Bat
+  138: Surveillance Camera
+  139: Cat
+  140: Jug
+  141: Broccoli
+  142: Piano
+  143: Pizza
+  144: Elephant
+  145: Skateboard
+  146: Surfboard
+  147: Gun
+  148: Skating and Skiing shoes
+  149: Gas stove
+  150: Donut
+  151: Bow Tie
+  152: Carrot
+  153: Toilet
+  154: Kite
+  155: Strawberry
+  156: Other Balls
+  157: Shovel
+  158: Pepper
+  159: Computer Box
+  160: Toilet Paper
+  161: Cleaning Products
+  162: Chopsticks
+  163: Microwave
+  164: Pigeon
+  165: Baseball
+  166: Cutting/chopping Board
+  167: Coffee Table
+  168: Side Table
+  169: Scissors
+  170: Marker
+  171: Pie
+  172: Ladder
+  173: Snowboard
+  174: Cookies
+  175: Radiator
+  176: Fire Hydrant
+  177: Basketball
+  178: Zebra
+  179: Grape
+  180: Giraffe
+  181: Potato
+  182: Sausage
+  183: Tricycle
+  184: Violin
+  185: Egg
+  186: Fire Extinguisher
+  187: Candy
+  188: Fire Truck
+  189: Billiards
+  190: Converter
+  191: Bathtub
+  192: Wheelchair
+  193: Golf Club
+  194: Briefcase
+  195: Cucumber
+  196: Cigar/Cigarette
+  197: Paint Brush
+  198: Pear
+  199: Heavy Truck
+  200: Hamburger
+  201: Extractor
+  202: Extension Cord
+  203: Tong
+  204: Tennis Racket
+  205: Folder
+  206: American Football
+  207: earphone
+  208: Mask
+  209: Kettle
+  210: Tennis
+  211: Ship
+  212: Swing
+  213: Coffee Machine
+  214: Slide
+  215: Carriage
+  216: Onion
+  217: Green beans
+  218: Projector
+  219: Frisbee
+  220: Washing Machine/Drying Machine
+  221: Chicken
+  222: Printer
+  223: Watermelon
+  224: Saxophone
+  225: Tissue
+  226: Toothbrush
+  227: Ice cream
+  228: Hot-air balloon
+  229: Cello
+  230: French Fries
+  231: Scale
+  232: Trophy
+  233: Cabbage
+  234: Hot dog
+  235: Blender
+  236: Peach
+  237: Rice
+  238: Wallet/Purse
+  239: Volleyball
+  240: Deer
+  241: Goose
+  242: Tape
+  243: Tablet
+  244: Cosmetics
+  245: Trumpet
+  246: Pineapple
+  247: Golf Ball
+  248: Ambulance
+  249: Parking meter
+  250: Mango
+  251: Key
+  252: Hurdle
+  253: Fishing Rod
+  254: Medal
+  255: Flute
+  256: Brush
+  257: Penguin
+  258: Megaphone
+  259: Corn
+  260: Lettuce
+  261: Garlic
+  262: Swan
+  263: Helicopter
+  264: Green Onion
+  265: Sandwich
+  266: Nuts
+  267: Speed Limit Sign
+  268: Induction Cooker
+  269: Broom
+  270: Trombone
+  271: Plum
+  272: Rickshaw
+  273: Goldfish
+  274: Kiwi fruit
+  275: Router/modem
+  276: Poker Card
+  277: Toaster
+  278: Shrimp
+  279: Sushi
+  280: Cheese
+  281: Notepaper
+  282: Cherry
+  283: Pliers
+  284: CD
+  285: Pasta
+  286: Hammer
+  287: Cue
+  288: Avocado
+  289: Hamimelon
+  290: Flask
+  291: Mushroom
+  292: Screwdriver
+  293: Soap
+  294: Recorder
+  295: Bear
+  296: Eggplant
+  297: Board Eraser
+  298: Coconut
+  299: Tape Measure/Ruler
+  300: Pig
+  301: Showerhead
+  302: Globe
+  303: Chips
+  304: Steak
+  305: Crosswalk Sign
+  306: Stapler
+  307: Camel
+  308: Formula 1
+  309: Pomegranate
+  310: Dishwasher
+  311: Crab
+  312: Hoverboard
+  313: Meat ball
+  314: Rice Cooker
+  315: Tuba
+  316: Calculator
+  317: Papaya
+  318: Antelope
+  319: Parrot
+  320: Seal
+  321: Butterfly
+  322: Dumbbell
+  323: Donkey
+  324: Lion
+  325: Urinal
+  326: Dolphin
+  327: Electric Drill
+  328: Hair Dryer
+  329: Egg tart
+  330: Jellyfish
+  331: Treadmill
+  332: Lighter
+  333: Grapefruit
+  334: Game board
+  335: Mop
+  336: Radish
+  337: Baozi
+  338: Target
+  339: French
+  340: Spring Rolls
+  341: Monkey
+  342: Rabbit
+  343: Pencil Case
+  344: Yak
+  345: Red Cabbage
+  346: Binoculars
+  347: Asparagus
+  348: Barbell
+  349: Scallop
+  350: Noddles
+  351: Comb
+  352: Dumpling
+  353: Oyster
+  354: Table Tennis paddle
+  355: Cosmetics Brush/Eyeliner Pencil
+  356: Chainsaw
+  357: Eraser
+  358: Lobster
+  359: Durian
+  360: Okra
+  361: Lipstick
+  362: Cosmetics Mirror
+  363: Curling
+  364: Table Tennis
+
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  from tqdm import tqdm
+
+  from utils.general import Path, check_requirements, download, np, xyxy2xywhn
+
+  check_requirements(('pycocotools>=2.0',))
+  from pycocotools.coco import COCO
+
+  # Make Directories
+  dir = Path(yaml['path'])  # dataset root dir
+  for p in 'images', 'labels':
+      (dir / p).mkdir(parents=True, exist_ok=True)
+      for q in 'train', 'val':
+          (dir / p / q).mkdir(parents=True, exist_ok=True)
+
+  # Train, Val Splits
+  for split, patches in [('train', 50 + 1), ('val', 43 + 1)]:
+      print(f"Processing {split} in {patches} patches ...")
+      images, labels = dir / 'images' / split, dir / 'labels' / split
+
+      # Download
+      url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/"
+      if split == 'train':
+          download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir, delete=False)  # annotations json
+          download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, delete=False, threads=8)
+      elif split == 'val':
+          download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir, delete=False)  # annotations json
+          download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, delete=False, threads=8)
+          download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, delete=False, threads=8)
+
+      # Move
+      for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'):
+          f.rename(images / f.name)  # move to /images/{split}
+
+      # Labels
+      coco = COCO(dir / f'zhiyuan_objv2_{split}.json')
+      names = [x["name"] for x in coco.loadCats(coco.getCatIds())]
+      for cid, cat in enumerate(names):
+          catIds = coco.getCatIds(catNms=[cat])
+          imgIds = coco.getImgIds(catIds=catIds)
+          for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'):
+              width, height = im["width"], im["height"]
+              path = Path(im["file_name"])  # image filename
+              try:
+                  with open(labels / path.with_suffix('.txt').name, 'a') as file:
+                      annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None)
+                      for a in coco.loadAnns(annIds):
+                          x, y, w, h = a['bbox']  # bounding box in xywh (xy top-left corner)
+                          xyxy = np.array([x, y, x + w, y + h])[None]  # pixels(1,4)
+                          x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0]  # normalized and clipped
+                          file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n")
+              except Exception as e:
+                  print(e)

data/SKU-110K.yaml

@@ -0,0 +1,53 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail
+# Example usage: python train.py --data SKU-110K.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── SKU-110K  ← downloads here (13.6 GB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/SKU-110K  # dataset root dir
+train: train.txt  # train images (relative to 'path')  8219 images
+val: val.txt  # val images (relative to 'path')  588 images
+test: test.txt  # test images (optional)  2936 images
+
+# Classes
+names:
+  0: object
+
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  import shutil
+  from tqdm import tqdm
+  from utils.general import np, pd, Path, download, xyxy2xywh
+
+
+  # Download
+  dir = Path(yaml['path'])  # dataset root dir
+  parent = Path(dir.parent)  # download dir
+  urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz']
+  download(urls, dir=parent, delete=False)
+
+  # Rename directories
+  if dir.exists():
+      shutil.rmtree(dir)
+  (parent / 'SKU110K_fixed').rename(dir)  # rename dir
+  (dir / 'labels').mkdir(parents=True, exist_ok=True)  # create labels dir
+
+  # Convert labels
+  names = 'image', 'x1', 'y1', 'x2', 'y2', 'class', 'image_width', 'image_height'  # column names
+  for d in 'annotations_train.csv', 'annotations_val.csv', 'annotations_test.csv':
+      x = pd.read_csv(dir / 'annotations' / d, names=names).values  # annotations
+      images, unique_images = x[:, 0], np.unique(x[:, 0])
+      with open((dir / d).with_suffix('.txt').__str__().replace('annotations_', ''), 'w') as f:
+          f.writelines(f'./images/{s}\n' for s in unique_images)
+      for im in tqdm(unique_images, desc=f'Converting {dir / d}'):
+          cls = 0  # single-class dataset
+          with open((dir / 'labels' / im).with_suffix('.txt'), 'a') as f:
+              for r in x[images == im]:
+                  w, h = r[6], r[7]  # image width, height
+                  xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0]  # instance
+                  f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n")  # write label

data/VOC.yaml

@@ -0,0 +1,100 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
+# Example usage: python train.py --data VOC.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── VOC  ← downloads here (2.8 GB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/VOC
+train: # train images (relative to 'path')  16551 images
+  - images/train2012
+  - images/train2007
+  - images/val2012
+  - images/val2007
+val: # val images (relative to 'path')  4952 images
+  - images/test2007
+test: # test images (optional)
+  - images/test2007
+
+# Classes
+names:
+  0: aeroplane
+  1: bicycle
+  2: bird
+  3: boat
+  4: bottle
+  5: bus
+  6: car
+  7: cat
+  8: chair
+  9: cow
+  10: diningtable
+  11: dog
+  12: horse
+  13: motorbike
+  14: person
+  15: pottedplant
+  16: sheep
+  17: sofa
+  18: train
+  19: tvmonitor
+
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  import xml.etree.ElementTree as ET
+
+  from tqdm import tqdm
+  from utils.general import download, Path
+
+
+  def convert_label(path, lb_path, year, image_id):
+      def convert_box(size, box):
+          dw, dh = 1. / size[0], 1. / size[1]
+          x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]
+          return x * dw, y * dh, w * dw, h * dh
+
+      in_file = open(path / f'VOC{year}/Annotations/{image_id}.xml')
+      out_file = open(lb_path, 'w')
+      tree = ET.parse(in_file)
+      root = tree.getroot()
+      size = root.find('size')
+      w = int(size.find('width').text)
+      h = int(size.find('height').text)
+
+      names = list(yaml['names'].values())  # names list
+      for obj in root.iter('object'):
+          cls = obj.find('name').text
+          if cls in names and int(obj.find('difficult').text) != 1:
+              xmlbox = obj.find('bndbox')
+              bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')])
+              cls_id = names.index(cls)  # class id
+              out_file.write(" ".join([str(a) for a in (cls_id, *bb)]) + '\n')
+
+
+  # Download
+  dir = Path(yaml['path'])  # dataset root dir
+  url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
+  urls = [f'{url}VOCtrainval_06-Nov-2007.zip',  # 446MB, 5012 images
+          f'{url}VOCtest_06-Nov-2007.zip',  # 438MB, 4953 images
+          f'{url}VOCtrainval_11-May-2012.zip']  # 1.95GB, 17126 images
+  download(urls, dir=dir / 'images', delete=False, curl=True, threads=3)
+
+  # Convert
+  path = dir / 'images/VOCdevkit'
+  for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'):
+      imgs_path = dir / 'images' / f'{image_set}{year}'
+      lbs_path = dir / 'labels' / f'{image_set}{year}'
+      imgs_path.mkdir(exist_ok=True, parents=True)
+      lbs_path.mkdir(exist_ok=True, parents=True)
+
+      with open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt') as f:
+          image_ids = f.read().strip().split()
+      for id in tqdm(image_ids, desc=f'{image_set}{year}'):
+          f = path / f'VOC{year}/JPEGImages/{id}.jpg'  # old img path
+          lb_path = (lbs_path / f.name).with_suffix('.txt')  # new label path
+          f.rename(imgs_path / f.name)  # move image
+          convert_label(path, lb_path, year, id)  # convert labels to YOLO format

data/VisDrone.yaml

@@ -0,0 +1,70 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
+# Example usage: python train.py --data VisDrone.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── VisDrone  ← downloads here (2.3 GB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/VisDrone  # dataset root dir
+train: VisDrone2019-DET-train/images  # train images (relative to 'path')  6471 images
+val: VisDrone2019-DET-val/images  # val images (relative to 'path')  548 images
+test: VisDrone2019-DET-test-dev/images  # test images (optional)  1610 images
+
+# Classes
+names:
+  0: pedestrian
+  1: people
+  2: bicycle
+  3: car
+  4: van
+  5: truck
+  6: tricycle
+  7: awning-tricycle
+  8: bus
+  9: motor
+
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  from utils.general import download, os, Path
+
+  def visdrone2yolo(dir):
+      from PIL import Image
+      from tqdm import tqdm
+
+      def convert_box(size, box):
+          # Convert VisDrone box to YOLO xywh box
+          dw = 1. / size[0]
+          dh = 1. / size[1]
+          return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh
+
+      (dir / 'labels').mkdir(parents=True, exist_ok=True)  # make labels directory
+      pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')
+      for f in pbar:
+          img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size
+          lines = []
+          with open(f, 'r') as file:  # read annotation.txt
+              for row in [x.split(',') for x in file.read().strip().splitlines()]:
+                  if row[4] == '0':  # VisDrone 'ignored regions' class 0
+                      continue
+                  cls = int(row[5]) - 1
+                  box = convert_box(img_size, tuple(map(int, row[:4])))
+                  lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
+                  with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl:
+                      fl.writelines(lines)  # write label.txt
+
+
+  # Download
+  dir = Path(yaml['path'])  # dataset root dir
+  urls = ['https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip',
+          'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip',
+          'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip',
+          'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip']
+  download(urls, dir=dir, curl=True, threads=4)
+
+  # Convert
+  for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
+      visdrone2yolo(dir / d)  # convert VisDrone annotations to YOLO labels

data/coco.yaml

@@ -0,0 +1,116 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# COCO 2017 dataset http://cocodataset.org by Microsoft
+# Example usage: python train.py --data coco.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── coco  ← downloads here (20.1 GB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco  # dataset root dir
+train: train2017.txt  # train images (relative to 'path') 118287 images
+val: val2017.txt  # val images (relative to 'path') 5000 images
+test: test-dev2017.txt  # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+
+
+# Download script/URL (optional)
+download: |
+  from utils.general import download, Path
+
+
+  # Download labels
+  segments = False  # segment or box labels
+  dir = Path(yaml['path'])  # dataset root dir
+  url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
+  urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')]  # labels
+  download(urls, dir=dir.parent)
+
+  # Download data
+  urls = ['http://images.cocodataset.org/zips/train2017.zip',  # 19G, 118k images
+          'http://images.cocodataset.org/zips/val2017.zip',  # 1G, 5k images
+          'http://images.cocodataset.org/zips/test2017.zip']  # 7G, 41k images (optional)
+  download(urls, dir=dir / 'images', threads=3)

data/coco128-seg.yaml

@@ -0,0 +1,101 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# COCO128-seg dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
+# Example usage: python train.py --data coco128.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── coco128-seg  ← downloads here (7 MB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco128-seg  # dataset root dir
+train: images/train2017  # train images (relative to 'path') 128 images
+val: images/train2017  # val images (relative to 'path') 128 images
+test:  # test images (optional)
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+
+
+# Download script/URL (optional)
+download: https://ultralytics.com/assets/coco128-seg.zip

data/coco128.yaml

@@ -0,0 +1,101 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
+# Example usage: python train.py --data coco128.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── coco128  ← downloads here (7 MB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco128  # dataset root dir
+train: images/train2017  # train images (relative to 'path') 128 images
+val: images/train2017  # val images (relative to 'path') 128 images
+test:  # test images (optional)
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+
+
+# Download script/URL (optional)
+download: https://ultralytics.com/assets/coco128.zip

data/hyps/hyp.Objects365.yaml

@@ -0,0 +1,34 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Hyperparameters for Objects365 training
+# python train.py --weights yolov5m.pt --data Objects365.yaml --evolve
+# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials
+
+lr0: 0.00258
+lrf: 0.17
+momentum: 0.779
+weight_decay: 0.00058
+warmup_epochs: 1.33
+warmup_momentum: 0.86
+warmup_bias_lr: 0.0711
+box: 0.0539
+cls: 0.299
+cls_pw: 0.825
+obj: 0.632
+obj_pw: 1.0
+iou_t: 0.2
+anchor_t: 3.44
+anchors: 3.2
+fl_gamma: 0.0
+hsv_h: 0.0188
+hsv_s: 0.704
+hsv_v: 0.36
+degrees: 0.0
+translate: 0.0902
+scale: 0.491
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+mosaic: 1.0
+mixup: 0.0
+copy_paste: 0.0

data/hyps/hyp.VOC.yaml

@@ -0,0 +1,40 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Hyperparameters for VOC training
+# python train.py --batch 128 --weights yolov5m6.pt --data VOC.yaml --epochs 50 --img 512 --hyp hyp.scratch-med.yaml --evolve
+# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials
+
+# YOLOv5 Hyperparameter Evolution Results
+# Best generation: 467
+# Last generation: 996
+#    metrics/precision,       metrics/recall,      metrics/mAP_0.5, metrics/mAP_0.5:0.95,         val/box_loss,         val/obj_loss,         val/cls_loss
+#              0.87729,              0.85125,              0.91286,              0.72664,            0.0076739,            0.0042529,            0.0013865
+
+lr0: 0.00334
+lrf: 0.15135
+momentum: 0.74832
+weight_decay: 0.00025
+warmup_epochs: 3.3835
+warmup_momentum: 0.59462
+warmup_bias_lr: 0.18657
+box: 0.02
+cls: 0.21638
+cls_pw: 0.5
+obj: 0.51728
+obj_pw: 0.67198
+iou_t: 0.2
+anchor_t: 3.3744
+fl_gamma: 0.0
+hsv_h: 0.01041
+hsv_s: 0.54703
+hsv_v: 0.27739
+degrees: 0.0
+translate: 0.04591
+scale: 0.75544
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+mosaic: 0.85834
+mixup: 0.04266
+copy_paste: 0.0
+anchors: 3.412

data/hyps/hyp.no-augmentation.yaml

@@ -0,0 +1,35 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Hyperparameters when using Albumentations frameworks
+# python train.py --hyp hyp.no-augmentation.yaml
+# See https://github.com/ultralytics/yolov5/pull/3882 for YOLOv5 + Albumentations Usage examples
+
+lr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)
+lrf: 0.1  # final OneCycleLR learning rate (lr0 * lrf)
+momentum: 0.937  # SGD momentum/Adam beta1
+weight_decay: 0.0005  # optimizer weight decay 5e-4
+warmup_epochs: 3.0  # warmup epochs (fractions ok)
+warmup_momentum: 0.8  # warmup initial momentum
+warmup_bias_lr: 0.1  # warmup initial bias lr
+box: 0.05  # box loss gain
+cls: 0.3  # cls loss gain
+cls_pw: 1.0  # cls BCELoss positive_weight
+obj: 0.7  # obj loss gain (scale with pixels)
+obj_pw: 1.0  # obj BCELoss positive_weight
+iou_t: 0.20  # IoU training threshold
+anchor_t: 4.0  # anchor-multiple threshold
+# anchors: 3  # anchors per output layer (0 to ignore)
+# this parameters are all zero since we want to use albumentation framework
+fl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)
+hsv_h: 0  # image HSV-Hue augmentation (fraction)
+hsv_s: 00  # image HSV-Saturation augmentation (fraction)
+hsv_v: 0  # image HSV-Value augmentation (fraction)
+degrees: 0.0  # image rotation (+/- deg)
+translate: 0  # image translation (+/- fraction)
+scale: 0  # image scale (+/- gain)
+shear: 0  # image shear (+/- deg)
+perspective: 0.0  # image perspective (+/- fraction), range 0-0.001
+flipud: 0.0  # image flip up-down (probability)
+fliplr: 0.0  # image flip left-right (probability)
+mosaic: 0.0  # image mosaic (probability)
+mixup: 0.0  # image mixup (probability)
+copy_paste: 0.0  # segment copy-paste (probability)

data/hyps/hyp.scratch-high.yaml

@@ -0,0 +1,34 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Hyperparameters for high-augmentation COCO training from scratch
+# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
+# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
+
+lr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)
+lrf: 0.1  # final OneCycleLR learning rate (lr0 * lrf)
+momentum: 0.937  # SGD momentum/Adam beta1
+weight_decay: 0.0005  # optimizer weight decay 5e-4
+warmup_epochs: 3.0  # warmup epochs (fractions ok)
+warmup_momentum: 0.8  # warmup initial momentum
+warmup_bias_lr: 0.1  # warmup initial bias lr
+box: 0.05  # box loss gain
+cls: 0.3  # cls loss gain
+cls_pw: 1.0  # cls BCELoss positive_weight
+obj: 0.7  # obj loss gain (scale with pixels)
+obj_pw: 1.0  # obj BCELoss positive_weight
+iou_t: 0.20  # IoU training threshold
+anchor_t: 4.0  # anchor-multiple threshold
+# anchors: 3  # anchors per output layer (0 to ignore)
+fl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)
+hsv_h: 0.015  # image HSV-Hue augmentation (fraction)
+hsv_s: 0.7  # image HSV-Saturation augmentation (fraction)
+hsv_v: 0.4  # image HSV-Value augmentation (fraction)
+degrees: 0.0  # image rotation (+/- deg)
+translate: 0.1  # image translation (+/- fraction)
+scale: 0.9  # image scale (+/- gain)
+shear: 0.0  # image shear (+/- deg)
+perspective: 0.0  # image perspective (+/- fraction), range 0-0.001
+flipud: 0.0  # image flip up-down (probability)
+fliplr: 0.5  # image flip left-right (probability)
+mosaic: 1.0  # image mosaic (probability)
+mixup: 0.1  # image mixup (probability)
+copy_paste: 0.1  # segment copy-paste (probability)

data/hyps/hyp.scratch-low.yaml

@@ -0,0 +1,34 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Hyperparameters for low-augmentation COCO training from scratch
+# python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear
+# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
+
+lr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)
+lrf: 0.01  # final OneCycleLR learning rate (lr0 * lrf)
+momentum: 0.937  # SGD momentum/Adam beta1
+weight_decay: 0.0005  # optimizer weight decay 5e-4
+warmup_epochs: 3.0  # warmup epochs (fractions ok)
+warmup_momentum: 0.8  # warmup initial momentum
+warmup_bias_lr: 0.1  # warmup initial bias lr
+box: 0.05  # box loss gain
+cls: 0.5  # cls loss gain
+cls_pw: 1.0  # cls BCELoss positive_weight
+obj: 1.0  # obj loss gain (scale with pixels)
+obj_pw: 1.0  # obj BCELoss positive_weight
+iou_t: 0.20  # IoU training threshold
+anchor_t: 4.0  # anchor-multiple threshold
+# anchors: 3  # anchors per output layer (0 to ignore)
+fl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)
+hsv_h: 0.015  # image HSV-Hue augmentation (fraction)
+hsv_s: 0.7  # image HSV-Saturation augmentation (fraction)
+hsv_v: 0.4  # image HSV-Value augmentation (fraction)
+degrees: 0.0  # image rotation (+/- deg)
+translate: 0.1  # image translation (+/- fraction)
+scale: 0.5  # image scale (+/- gain)
+shear: 0.0  # image shear (+/- deg)
+perspective: 0.0  # image perspective (+/- fraction), range 0-0.001
+flipud: 0.0  # image flip up-down (probability)
+fliplr: 0.5  # image flip left-right (probability)
+mosaic: 1.0  # image mosaic (probability)
+mixup: 0.0  # image mixup (probability)
+copy_paste: 0.0  # segment copy-paste (probability)

data/hyps/hyp.scratch-med.yaml

@@ -0,0 +1,34 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Hyperparameters for medium-augmentation COCO training from scratch
+# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
+# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
+
+lr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)
+lrf: 0.1  # final OneCycleLR learning rate (lr0 * lrf)
+momentum: 0.937  # SGD momentum/Adam beta1
+weight_decay: 0.0005  # optimizer weight decay 5e-4
+warmup_epochs: 3.0  # warmup epochs (fractions ok)
+warmup_momentum: 0.8  # warmup initial momentum
+warmup_bias_lr: 0.1  # warmup initial bias lr
+box: 0.05  # box loss gain
+cls: 0.3  # cls loss gain
+cls_pw: 1.0  # cls BCELoss positive_weight
+obj: 0.7  # obj loss gain (scale with pixels)
+obj_pw: 1.0  # obj BCELoss positive_weight
+iou_t: 0.20  # IoU training threshold
+anchor_t: 4.0  # anchor-multiple threshold
+# anchors: 3  # anchors per output layer (0 to ignore)
+fl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)
+hsv_h: 0.015  # image HSV-Hue augmentation (fraction)
+hsv_s: 0.7  # image HSV-Saturation augmentation (fraction)
+hsv_v: 0.4  # image HSV-Value augmentation (fraction)
+degrees: 0.0  # image rotation (+/- deg)
+translate: 0.1  # image translation (+/- fraction)
+scale: 0.9  # image scale (+/- gain)
+shear: 0.0  # image shear (+/- deg)
+perspective: 0.0  # image perspective (+/- fraction), range 0-0.001
+flipud: 0.0  # image flip up-down (probability)
+fliplr: 0.5  # image flip left-right (probability)
+mosaic: 1.0  # image mosaic (probability)
+mixup: 0.1  # image mixup (probability)
+copy_paste: 0.0  # segment copy-paste (probability)

data/scripts/download_weights.sh

@@ -0,0 +1,22 @@
+#!/bin/bash
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Download latest models from https://github.com/ultralytics/yolov5/releases
+# Example usage: bash data/scripts/download_weights.sh
+# parent
+# └── yolov5
+#     ├── yolov5s.pt  ← downloads here
+#     ├── yolov5m.pt
+#     └── ...
+
+python - <<EOF
+from utils.downloads import attempt_download
+
+p5 = list('nsmlx')  # P5 models
+p6 = [f'{x}6' for x in p5]  # P6 models
+cls = [f'{x}-cls' for x in p5]  # classification models
+seg = [f'{x}-seg' for x in p5]  # classification models
+
+for x in p5 + p6 + cls + seg:
+    attempt_download(f'weights/yolov5{x}.pt')
+
+EOF

data/scripts/get_coco.sh

@@ -0,0 +1,56 @@
+#!/bin/bash
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Download COCO 2017 dataset http://cocodataset.org
+# Example usage: bash data/scripts/get_coco.sh
+# parent
+# ├── yolov5
+# └── datasets
+#     └── coco  ← downloads here
+
+# Arguments (optional) Usage: bash data/scripts/get_coco.sh --train --val --test --segments
+if [ "$#" -gt 0 ]; then
+  for opt in "$@"; do
+    case "${opt}" in
+    --train) train=true ;;
+    --val) val=true ;;
+    --test) test=true ;;
+    --segments) segments=true ;;
+    esac
+  done
+else
+  train=true
+  val=true
+  test=false
+  segments=false
+fi
+
+# Download/unzip labels
+d='../datasets' # unzip directory
+url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
+if [ "$segments" == "true" ]; then
+  f='coco2017labels-segments.zip' # 168 MB
+else
+  f='coco2017labels.zip' # 46 MB
+fi
+echo 'Downloading' $url$f ' ...'
+curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
+
+# Download/unzip images
+d='../datasets/coco/images' # unzip directory
+url=http://images.cocodataset.org/zips/
+if [ "$train" == "true" ]; then
+  f='train2017.zip' # 19G, 118k images
+  echo 'Downloading' $url$f '...'
+  curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
+fi
+if [ "$val" == "true" ]; then
+  f='val2017.zip' # 1G, 5k images
+  echo 'Downloading' $url$f '...'
+  curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
+fi
+if [ "$test" == "true" ]; then
+  f='test2017.zip' # 7G, 41k images (optional)
+  echo 'Downloading' $url$f '...'
+  curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
+fi
+wait # finish background tasks

data/scripts/get_coco128.sh

@@ -0,0 +1,17 @@
+#!/bin/bash
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Download COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
+# Example usage: bash data/scripts/get_coco128.sh
+# parent
+# ├── yolov5
+# └── datasets
+#     └── coco128  ← downloads here
+
+# Download/unzip images and labels
+d='../datasets' # unzip directory
+url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
+f='coco128.zip' # or 'coco128-segments.zip', 68 MB
+echo 'Downloading' $url$f ' ...'
+curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
+
+wait # finish background tasks

data/scripts/get_imagenet.sh

@@ -0,0 +1,51 @@
+#!/bin/bash
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Download ILSVRC2012 ImageNet dataset https://image-net.org
+# Example usage: bash data/scripts/get_imagenet.sh
+# parent
+# ├── yolov5
+# └── datasets
+#     └── imagenet  ← downloads here
+
+# Arguments (optional) Usage: bash data/scripts/get_imagenet.sh --train --val
+if [ "$#" -gt 0 ]; then
+  for opt in "$@"; do
+    case "${opt}" in
+    --train) train=true ;;
+    --val) val=true ;;
+    esac
+  done
+else
+  train=true
+  val=true
+fi
+
+# Make dir
+d='../datasets/imagenet' # unzip directory
+mkdir -p $d && cd $d
+
+# Download/unzip train
+if [ "$train" == "true" ]; then
+  wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_train.tar # download 138G, 1281167 images
+  mkdir train && mv ILSVRC2012_img_train.tar train/ && cd train
+  tar -xf ILSVRC2012_img_train.tar && rm -f ILSVRC2012_img_train.tar
+  find . -name "*.tar" | while read NAME; do
+    mkdir -p "${NAME%.tar}"
+    tar -xf "${NAME}" -C "${NAME%.tar}"
+    rm -f "${NAME}"
+  done
+  cd ..
+fi
+
+# Download/unzip val
+if [ "$val" == "true" ]; then
+  wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar # download 6.3G, 50000 images
+  mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xf ILSVRC2012_img_val.tar
+  wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash # move into subdirs
+fi
+
+# Delete corrupted image (optional: PNG under JPEG name that may cause dataloaders to fail)
+# rm train/n04266014/n04266014_10835.JPEG
+
+# TFRecords (optional)
+# wget https://raw.githubusercontent.com/tensorflow/models/master/research/slim/datasets/imagenet_lsvrc_2015_synsets.txt

data/xView.yaml

@@ -0,0 +1,153 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# DIUx xView 2018 Challenge https://challenge.xviewdataset.org by U.S. National Geospatial-Intelligence Agency (NGA)
+# --------  DOWNLOAD DATA MANUALLY and jar xf val_images.zip to 'datasets/xView' before running train command!  --------
+# Example usage: python train.py --data xView.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── xView  ← downloads here (20.7 GB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/xView  # dataset root dir
+train: images/autosplit_train.txt  # train images (relative to 'path') 90% of 847 train images
+val: images/autosplit_val.txt  # train images (relative to 'path') 10% of 847 train images
+
+# Classes
+names:
+  0: Fixed-wing Aircraft
+  1: Small Aircraft
+  2: Cargo Plane
+  3: Helicopter
+  4: Passenger Vehicle
+  5: Small Car
+  6: Bus
+  7: Pickup Truck
+  8: Utility Truck
+  9: Truck
+  10: Cargo Truck
+  11: Truck w/Box
+  12: Truck Tractor
+  13: Trailer
+  14: Truck w/Flatbed
+  15: Truck w/Liquid
+  16: Crane Truck
+  17: Railway Vehicle
+  18: Passenger Car
+  19: Cargo Car
+  20: Flat Car
+  21: Tank car
+  22: Locomotive
+  23: Maritime Vessel
+  24: Motorboat
+  25: Sailboat
+  26: Tugboat
+  27: Barge
+  28: Fishing Vessel
+  29: Ferry
+  30: Yacht
+  31: Container Ship
+  32: Oil Tanker
+  33: Engineering Vehicle
+  34: Tower crane
+  35: Container Crane
+  36: Reach Stacker
+  37: Straddle Carrier
+  38: Mobile Crane
+  39: Dump Truck
+  40: Haul Truck
+  41: Scraper/Tractor
+  42: Front loader/Bulldozer
+  43: Excavator
+  44: Cement Mixer
+  45: Ground Grader
+  46: Hut/Tent
+  47: Shed
+  48: Building
+  49: Aircraft Hangar
+  50: Damaged Building
+  51: Facility
+  52: Construction Site
+  53: Vehicle Lot
+  54: Helipad
+  55: Storage Tank
+  56: Shipping container lot
+  57: Shipping Container
+  58: Pylon
+  59: Tower
+
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  import json
+  import os
+  from pathlib import Path
+
+  import numpy as np
+  from PIL import Image
+  from tqdm import tqdm
+
+  from utils.dataloaders import autosplit
+  from utils.general import download, xyxy2xywhn
+
+
+  def convert_labels(fname=Path('xView/xView_train.geojson')):
+      # Convert xView geoJSON labels to YOLO format
+      path = fname.parent
+      with open(fname) as f:
+          print(f'Loading {fname}...')
+          data = json.load(f)
+
+      # Make dirs
+      labels = Path(path / 'labels' / 'train')
+      os.system(f'rm -rf {labels}')
+      labels.mkdir(parents=True, exist_ok=True)
+
+      # xView classes 11-94 to 0-59
+      xview_class2index = [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 1, 2, -1, 3, -1, 4, 5, 6, 7, 8, -1, 9, 10, 11,
+                           12, 13, 14, 15, -1, -1, 16, 17, 18, 19, 20, 21, 22, -1, 23, 24, 25, -1, 26, 27, -1, 28, -1,
+                           29, 30, 31, 32, 33, 34, 35, 36, 37, -1, 38, 39, 40, 41, 42, 43, 44, 45, -1, -1, -1, -1, 46,
+                           47, 48, 49, -1, 50, 51, -1, 52, -1, -1, -1, 53, 54, -1, 55, -1, -1, 56, -1, 57, -1, 58, 59]
+
+      shapes = {}
+      for feature in tqdm(data['features'], desc=f'Converting {fname}'):
+          p = feature['properties']
+          if p['bounds_imcoords']:
+              id = p['image_id']
+              file = path / 'train_images' / id
+              if file.exists():  # 1395.tif missing
+                  try:
+                      box = np.array([int(num) for num in p['bounds_imcoords'].split(",")])
+                      assert box.shape[0] == 4, f'incorrect box shape {box.shape[0]}'
+                      cls = p['type_id']
+                      cls = xview_class2index[int(cls)]  # xView class to 0-60
+                      assert 59 >= cls >= 0, f'incorrect class index {cls}'
+
+                      # Write YOLO label
+                      if id not in shapes:
+                          shapes[id] = Image.open(file).size
+                      box = xyxy2xywhn(box[None].astype(np.float), w=shapes[id][0], h=shapes[id][1], clip=True)
+                      with open((labels / id).with_suffix('.txt'), 'a') as f:
+                          f.write(f"{cls} {' '.join(f'{x:.6f}' for x in box[0])}\n")  # write label.txt
+                  except Exception as e:
+                      print(f'WARNING: skipping one label for {file}: {e}')
+
+
+  # Download manually from https://challenge.xviewdataset.org
+  dir = Path(yaml['path'])  # dataset root dir
+  # urls = ['https://d307kc0mrhucc3.cloudfront.net/train_labels.zip',  # train labels
+  #         'https://d307kc0mrhucc3.cloudfront.net/train_images.zip',  # 15G, 847 train images
+  #         'https://d307kc0mrhucc3.cloudfront.net/val_images.zip']  # 5G, 282 val images (no labels)
+  # download(urls, dir=dir, delete=False)
+
+  # Convert labels
+  convert_labels(dir / 'xView_train.geojson')
+
+  # Move images
+  images = Path(dir / 'images')
+  images.mkdir(parents=True, exist_ok=True)
+  Path(dir / 'train_images').rename(dir / 'images' / 'train')
+  Path(dir / 'val_images').rename(dir / 'images' / 'val')
+
+  # Split
+  autosplit(dir / 'images' / 'train')

detect.py

@@ -0,0 +1,460 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
+
+Usage - sources:
+    $ python detect.py --weights yolov5s.pt --source 0                               # webcam
+                                                     img.jpg                         # image
+                                                     vid.mp4                         # video
+                                                     screen                          # screenshot
+                                                     path/                           # directory
+                                                     list.txt                        # list of images
+                                                     list.streams                    # list of streams
+                                                     'path/*.jpg'                    # glob
+                                                     'https://youtu.be/Zgi9g1ksQHc'  # YouTube
+                                                     'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
+
+Usage - formats:
+    $ python detect.py --weights yolov5s.pt                 # PyTorch
+                                 yolov5s.torchscript        # TorchScript
+                                 yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
+                                 yolov5s_openvino_model     # OpenVINO
+                                 yolov5s.engine             # TensorRT
+                                 yolov5s.mlmodel            # CoreML (macOS-only)
+                                 yolov5s_saved_model        # TensorFlow SavedModel
+                                 yolov5s.pb                 # TensorFlow GraphDef
+                                 yolov5s.tflite             # TensorFlow Lite
+                                 yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
+                                 yolov5s_paddle_model       # PaddlePaddle
+"""
+
+import argparse
+import os
+import platform
+import sys
+from pathlib import Path
+
+import torch
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[0]  # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
+
+from models.common import DetectMultiBackend
+from utils.dataloaders import (
+    IMG_FORMATS,
+    VID_FORMATS,
+    LoadImages,
+    LoadScreenshots,
+    LoadStreams,
+)
+from utils.general import (
+    LOGGER,
+    Profile,
+    check_file,
+    check_img_size,
+    check_imshow,
+    check_requirements,
+    colorstr,
+    cv2,
+    increment_path,
+    non_max_suppression,
+    print_args,
+    scale_boxes,
+    strip_optimizer,
+    xyxy2xywh,
+)
+from utils.plots import Annotator, colors, save_one_box
+from utils.torch_utils import select_device, smart_inference_mode
+
+
+@smart_inference_mode()
+def run(
+    weights=ROOT / "yolov5s.pt",  # model path or triton URL
+    source=ROOT / "data/images",  # file/dir/URL/glob/screen/0(webcam)
+    data=ROOT / "data/coco128.yaml",  # dataset.yaml path
+    imgsz=(640, 640),  # inference size (height, width)
+    conf_thres=0.25,  # confidence threshold
+    iou_thres=0.45,  # NMS IOU threshold
+    max_det=1000,  # maximum detections per image
+    device="",  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+    view_img=False,  # show results
+    save_txt=False,  # save results to *.txt
+    save_conf=False,  # save confidences in --save-txt labels
+    save_crop=False,  # save cropped prediction boxes
+    nosave=False,  # do not save images/videos
+    classes=None,  # filter by class: --class 0, or --class 0 2 3
+    agnostic_nms=False,  # class-agnostic NMS
+    augment=False,  # augmented inference
+    visualize=False,  # visualize features
+    update=False,  # update all models
+    project=ROOT / "runs/detect",  # save results to project/name
+    name="exp",  # save results to project/name
+    exist_ok=False,  # existing project/name ok, do not increment
+    line_thickness=3,  # bounding box thickness (pixels)
+    hide_labels=False,  # hide labels
+    hide_conf=False,  # hide confidences
+    half=False,  # use FP16 half-precision inference
+    dnn=False,  # use OpenCV DNN for ONNX inference
+    vid_stride=1,  # video frame-rate stride
+):
+    source = str(source)
+    save_img = not nosave and not source.endswith(
+        ".txt"
+    )  # save inference images
+    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
+    is_url = source.lower().startswith(
+        ("rtsp://", "rtmp://", "http://", "https://")
+    )
+    webcam = (
+        source.isnumeric()
+        or source.endswith(".streams")
+        or (is_url and not is_file)
+    )
+    screenshot = source.lower().startswith("screen")
+    if is_url and is_file:
+        source = check_file(source)  # download
+
+    # Directories
+    save_dir = increment_path(
+        Path(project) / name, exist_ok=exist_ok
+    )  # increment run
+    (save_dir / "labels" if save_txt else save_dir).mkdir(
+        parents=True, exist_ok=True
+    )  # make dir
+
+    # Load model
+    device = select_device(device)
+    model = DetectMultiBackend(
+        weights, device=device, dnn=dnn, data=data, fp16=half
+    )
+    stride, names, pt = model.stride, model.names, model.pt
+    imgsz = check_img_size(imgsz, s=stride)  # check image size
+
+    # Dataloader
+    bs = 1  # batch_size
+    if webcam:
+        view_img = check_imshow(warn=True)
+        dataset = LoadStreams(
+            source,
+            img_size=imgsz,
+            stride=stride,
+            auto=pt,
+            vid_stride=vid_stride,
+        )
+        bs = len(dataset)
+    elif screenshot:
+        dataset = LoadScreenshots(
+            source, img_size=imgsz, stride=stride, auto=pt
+        )
+    else:
+        dataset = LoadImages(
+            source,
+            img_size=imgsz,
+            stride=stride,
+            auto=pt,
+            vid_stride=vid_stride,
+        )
+    vid_path, vid_writer = [None] * bs, [None] * bs
+
+    # Run inference
+    model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz))  # warmup
+    seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
+    for path, im, im0s, vid_cap, s in dataset:
+        with dt[0]:
+            im = torch.from_numpy(im).to(model.device)
+            im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
+            im /= 255  # 0 - 255 to 0.0 - 1.0
+            if len(im.shape) == 3:
+                im = im[None]  # expand for batch dim
+
+        # Inference
+        with dt[1]:
+            visualize = (
+                increment_path(save_dir / Path(path).stem, mkdir=True)
+                if visualize
+                else False
+            )
+            pred = model(im, augment=augment, visualize=visualize)
+
+        # NMS
+        with dt[2]:
+            pred = non_max_suppression(
+                pred,
+                conf_thres,
+                iou_thres,
+                classes,
+                agnostic_nms,
+                max_det=max_det,
+            )
+
+        # Second-stage classifier (optional)
+        # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
+
+        # Process predictions
+        for i, det in enumerate(pred):  # per image
+            seen += 1
+            if webcam:  # batch_size >= 1
+                p, im0, frame = path[i], im0s[i].copy(), dataset.count
+                s += f"{i}: "
+            else:
+                p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0)
+
+            p = Path(p)  # to Path
+            save_path = str(save_dir / p.name)  # im.jpg
+            txt_path = str(save_dir / "labels" / p.stem) + (
+                "" if dataset.mode == "image" else f"_{frame}"
+            )  # im.txt
+            s += "%gx%g " % im.shape[2:]  # print string
+            gn = torch.tensor(im0.shape)[
+                [1, 0, 1, 0]
+            ]  # normalization gain whwh
+            imc = im0.copy() if save_crop else im0  # for save_crop
+            annotator = Annotator(
+                im0, line_width=line_thickness, example=str(names)
+            )
+            if len(det):
+                # Rescale boxes from img_size to im0 size
+                det[:, :4] = scale_boxes(
+                    im.shape[2:], det[:, :4], im0.shape
+                ).round()
+
+                # Print results
+                for c in det[:, 5].unique():
+                    n = (det[:, 5] == c).sum()  # detections per class
+                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string
+
+                # Write results
+                for *xyxy, conf, cls in reversed(det):
+                    if save_txt:  # Write to file
+                        xywh = (
+                            (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn)
+                            .view(-1)
+                            .tolist()
+                        )  # normalized xywh
+                        line = (
+                            (cls, *xywh, conf) if save_conf else (cls, *xywh)
+                        )  # label format
+                        with open(f"{txt_path}.txt", "a") as f:
+                            f.write(("%g " * len(line)).rstrip() % line + "\n")
+
+                    if save_img or save_crop or view_img:  # Add bbox to image
+                        c = int(cls)  # integer class
+                        label = (
+                            None
+                            if hide_labels
+                            else (
+                                names[c]
+                                if hide_conf
+                                else f"{names[c]} {conf:.2f}"
+                            )
+                        )
+                        annotator.box_label(xyxy, label, color=colors(c, True))
+                    if save_crop:
+                        save_one_box(
+                            xyxy,
+                            imc,
+                            file=save_dir
+                            / "crops"
+                            / names[c]
+                            / f"{p.stem}.jpg",
+                            BGR=True,
+                        )
+
+            # Stream results
+            im0 = annotator.result()
+            if view_img:
+                if platform.system() == "Linux" and p not in windows:
+                    windows.append(p)
+                    cv2.namedWindow(
+                        str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO
+                    )  # allow window resize (Linux)
+                    cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
+                cv2.imshow(str(p), im0)
+                cv2.waitKey(1)  # 1 millisecond
+
+            # Save results (image with detections)
+            if save_img:
+                if dataset.mode == "image":
+                    cv2.imwrite(save_path, im0)
+                else:  # 'video' or 'stream'
+                    if vid_path[i] != save_path:  # new video
+                        vid_path[i] = save_path
+                        if isinstance(vid_writer[i], cv2.VideoWriter):
+                            vid_writer[
+                                i
+                            ].release()  # release previous video writer
+                        if vid_cap:  # video
+                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
+                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+                        else:  # stream
+                            fps, w, h = 30, im0.shape[1], im0.shape[0]
+                        save_path = str(
+                            Path(save_path).with_suffix(".mp4")
+                        )  # force *.mp4 suffix on results videos
+                        vid_writer[i] = cv2.VideoWriter(
+                            save_path,
+                            cv2.VideoWriter_fourcc(*"mp4v"),
+                            fps,
+                            (w, h),
+                        )
+                    vid_writer[i].write(im0)
+
+        # Print time (inference-only)
+        LOGGER.info(
+            f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms"
+        )
+
+    # Print results
+    t = tuple(x.t / seen * 1e3 for x in dt)  # speeds per image
+    LOGGER.info(
+        f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}"
+        % t
+    )
+    if save_txt or save_img:
+        s = (
+            f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}"
+            if save_txt
+            else ""
+        )
+        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
+    if update:
+        strip_optimizer(
+            weights[0]
+        )  # update model (to fix SourceChangeWarning)
+
+
+def parse_opt():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--weights",
+        nargs="+",
+        type=str,
+        default=ROOT / "yolov5s.pt",
+        help="model path or triton URL",
+    )
+    parser.add_argument(
+        "--source",
+        type=str,
+        default=ROOT / "data/images",
+        help="file/dir/URL/glob/screen/0(webcam)",
+    )
+    parser.add_argument(
+        "--data",
+        type=str,
+        default=ROOT / "data/coco128.yaml",
+        help="(optional) dataset.yaml path",
+    )
+    parser.add_argument(
+        "--imgsz",
+        "--img",
+        "--img-size",
+        nargs="+",
+        type=int,
+        default=[640],
+        help="inference size h,w",
+    )
+    parser.add_argument(
+        "--conf-thres", type=float, default=0.25, help="confidence threshold"
+    )
+    parser.add_argument(
+        "--iou-thres", type=float, default=0.45, help="NMS IoU threshold"
+    )
+    parser.add_argument(
+        "--max-det",
+        type=int,
+        default=1000,
+        help="maximum detections per image",
+    )
+    parser.add_argument(
+        "--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu"
+    )
+    parser.add_argument("--view-img", action="store_true", help="show results")
+    parser.add_argument(
+        "--save-txt", action="store_true", help="save results to *.txt"
+    )
+    parser.add_argument(
+        "--save-conf",
+        action="store_true",
+        help="save confidences in --save-txt labels",
+    )
+    parser.add_argument(
+        "--save-crop",
+        action="store_true",
+        help="save cropped prediction boxes",
+    )
+    parser.add_argument(
+        "--nosave", action="store_true", help="do not save images/videos"
+    )
+    parser.add_argument(
+        "--classes",
+        nargs="+",
+        type=int,
+        help="filter by class: --classes 0, or --classes 0 2 3",
+    )
+    parser.add_argument(
+        "--agnostic-nms", action="store_true", help="class-agnostic NMS"
+    )
+    parser.add_argument(
+        "--augment", action="store_true", help="augmented inference"
+    )
+    parser.add_argument(
+        "--visualize", action="store_true", help="visualize features"
+    )
+    parser.add_argument(
+        "--update", action="store_true", help="update all models"
+    )
+    parser.add_argument(
+        "--project",
+        default=ROOT / "runs/detect",
+        help="save results to project/name",
+    )
+    parser.add_argument(
+        "--name", default="exp", help="save results to project/name"
+    )
+    parser.add_argument(
+        "--exist-ok",
+        action="store_true",
+        help="existing project/name ok, do not increment",
+    )
+    parser.add_argument(
+        "--line-thickness",
+        default=3,
+        type=int,
+        help="bounding box thickness (pixels)",
+    )
+    parser.add_argument(
+        "--hide-labels", default=False, action="store_true", help="hide labels"
+    )
+    parser.add_argument(
+        "--hide-conf",
+        default=False,
+        action="store_true",
+        help="hide confidences",
+    )
+    parser.add_argument(
+        "--half", action="store_true", help="use FP16 half-precision inference"
+    )
+    parser.add_argument(
+        "--dnn", action="store_true", help="use OpenCV DNN for ONNX inference"
+    )
+    parser.add_argument(
+        "--vid-stride", type=int, default=1, help="video frame-rate stride"
+    )
+    opt = parser.parse_args()
+    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
+    print_args(vars(opt))
+    return opt
+
+
+def main(opt):
+    check_requirements(exclude=("tensorboard", "thop"))
+    run(**vars(opt))
+
+
+if __name__ == "__main__":
+    opt = parse_opt()
+    main(opt)

export.py

@@ -0,0 +1,1013 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit
+
+Format                      | `export.py --include`         | Model
+---                         | ---                           | ---
+PyTorch                     | -                             | yolov5s.pt
+TorchScript                 | `torchscript`                 | yolov5s.torchscript
+ONNX                        | `onnx`                        | yolov5s.onnx
+OpenVINO                    | `openvino`                    | yolov5s_openvino_model/
+TensorRT                    | `engine`                      | yolov5s.engine
+CoreML                      | `coreml`                      | yolov5s.mlmodel
+TensorFlow SavedModel       | `saved_model`                 | yolov5s_saved_model/
+TensorFlow GraphDef         | `pb`                          | yolov5s.pb
+TensorFlow Lite             | `tflite`                      | yolov5s.tflite
+TensorFlow Edge TPU         | `edgetpu`                     | yolov5s_edgetpu.tflite
+TensorFlow.js               | `tfjs`                        | yolov5s_web_model/
+PaddlePaddle                | `paddle`                      | yolov5s_paddle_model/
+
+Requirements:
+    $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu  # CPU
+    $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow  # GPU
+
+Usage:
+    $ python export.py --weights yolov5s.pt --include torchscript onnx openvino engine coreml tflite ...
+
+Inference:
+    $ python detect.py --weights yolov5s.pt                 # PyTorch
+                                 yolov5s.torchscript        # TorchScript
+                                 yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
+                                 yolov5s_openvino_model     # OpenVINO
+                                 yolov5s.engine             # TensorRT
+                                 yolov5s.mlmodel            # CoreML (macOS-only)
+                                 yolov5s_saved_model        # TensorFlow SavedModel
+                                 yolov5s.pb                 # TensorFlow GraphDef
+                                 yolov5s.tflite             # TensorFlow Lite
+                                 yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
+                                 yolov5s_paddle_model       # PaddlePaddle
+
+TensorFlow.js:
+    $ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example
+    $ npm install
+    $ ln -s ../../yolov5/yolov5s_web_model public/yolov5s_web_model
+    $ npm start
+"""
+
+import argparse
+import contextlib
+import json
+import os
+import platform
+import re
+import subprocess
+import sys
+import time
+import warnings
+from pathlib import Path
+
+import pandas as pd
+import torch
+from torch.utils.mobile_optimizer import optimize_for_mobile
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[0]  # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+if platform.system() != "Windows":
+    ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
+
+from models.experimental import attempt_load
+from models.yolo import ClassificationModel, Detect, DetectionModel, SegmentationModel
+from utils.dataloaders import LoadImages
+from utils.general import (
+    LOGGER,
+    Profile,
+    check_dataset,
+    check_img_size,
+    check_requirements,
+    check_version,
+    check_yaml,
+    colorstr,
+    file_size,
+    get_default_args,
+    print_args,
+    url2file,
+    yaml_save,
+)
+from utils.torch_utils import select_device, smart_inference_mode
+
+MACOS = platform.system() == "Darwin"  # macOS environment
+
+
+def export_formats():
+    # YOLOv5 export formats
+    x = [
+        ["PyTorch", "-", ".pt", True, True],
+        ["TorchScript", "torchscript", ".torchscript", True, True],
+        ["ONNX", "onnx", ".onnx", True, True],
+        ["OpenVINO", "openvino", "_openvino_model", True, False],
+        ["TensorRT", "engine", ".engine", False, True],
+        ["CoreML", "coreml", ".mlmodel", True, False],
+        ["TensorFlow SavedModel", "saved_model", "_saved_model", True, True],
+        ["TensorFlow GraphDef", "pb", ".pb", True, True],
+        ["TensorFlow Lite", "tflite", ".tflite", True, False],
+        ["TensorFlow Edge TPU", "edgetpu", "_edgetpu.tflite", False, False],
+        ["TensorFlow.js", "tfjs", "_web_model", False, False],
+        ["PaddlePaddle", "paddle", "_paddle_model", True, True],
+    ]
+    return pd.DataFrame(
+        x, columns=["Format", "Argument", "Suffix", "CPU", "GPU"]
+    )
+
+
+def try_export(inner_func):
+    # YOLOv5 export decorator, i..e @try_export
+    inner_args = get_default_args(inner_func)
+
+    def outer_func(*args, **kwargs):
+        prefix = inner_args["prefix"]
+        try:
+            with Profile() as dt:
+                f, model = inner_func(*args, **kwargs)
+            LOGGER.info(
+                f"{prefix} export success ✅ {dt.t:.1f}s, saved as {f} ({file_size(f):.1f} MB)"
+            )
+            return f, model
+        except Exception as e:
+            LOGGER.info(f"{prefix} export failure ❌ {dt.t:.1f}s: {e}")
+            return None, None
+
+    return outer_func
+
+
+@try_export
+def export_torchscript(
+    model, im, file, optimize, prefix=colorstr("TorchScript:")
+):
+    # YOLOv5 TorchScript model export
+    LOGGER.info(
+        f"\n{prefix} starting export with torch {torch.__version__}..."
+    )
+    f = file.with_suffix(".torchscript")
+
+    ts = torch.jit.trace(model, im, strict=False)
+    d = {
+        "shape": im.shape,
+        "stride": int(max(model.stride)),
+        "names": model.names,
+    }
+    extra_files = {"config.txt": json.dumps(d)}  # torch._C.ExtraFilesMap()
+    if (
+        optimize
+    ):  # https://pytorch.org/tutorials/recipes/mobile_interpreter.html
+        optimize_for_mobile(ts)._save_for_lite_interpreter(
+            str(f), _extra_files=extra_files
+        )
+    else:
+        ts.save(str(f), _extra_files=extra_files)
+    return f, None
+
+
+@try_export
+def export_onnx(
+    model, im, file, opset, dynamic, simplify, prefix=colorstr("ONNX:")
+):
+    # YOLOv5 ONNX export
+    check_requirements("onnx>=1.12.0")
+    import onnx
+
+    LOGGER.info(f"\n{prefix} starting export with onnx {onnx.__version__}...")
+    f = file.with_suffix(".onnx")
+
+    output_names = (
+        ["output0", "output1"]
+        if isinstance(model, SegmentationModel)
+        else ["output0"]
+    )
+    if dynamic:
+        dynamic = {
+            "images": {0: "batch", 2: "height", 3: "width"}
+        }  # shape(1,3,640,640)
+        if isinstance(model, SegmentationModel):
+            dynamic["output0"] = {
+                0: "batch",
+                1: "anchors",
+            }  # shape(1,25200,85)
+            dynamic["output1"] = {
+                0: "batch",
+                2: "mask_height",
+                3: "mask_width",
+            }  # shape(1,32,160,160)
+        elif isinstance(model, DetectionModel):
+            dynamic["output0"] = {
+                0: "batch",
+                1: "anchors",
+            }  # shape(1,25200,85)
+
+    torch.onnx.export(
+        model.cpu()
+        if dynamic
+        else model,  # --dynamic only compatible with cpu
+        im.cpu() if dynamic else im,
+        f,
+        verbose=False,
+        opset_version=opset,
+        do_constant_folding=True,  # WARNING: DNN inference with torch>=1.12 may require do_constant_folding=False
+        input_names=["images"],
+        output_names=output_names,
+        dynamic_axes=dynamic or None,
+    )
+
+    # Checks
+    model_onnx = onnx.load(f)  # load onnx model
+    onnx.checker.check_model(model_onnx)  # check onnx model
+
+    # Metadata
+    d = {"stride": int(max(model.stride)), "names": model.names}
+    for k, v in d.items():
+        meta = model_onnx.metadata_props.add()
+        meta.key, meta.value = k, str(v)
+    onnx.save(model_onnx, f)
+
+    # Simplify
+    if simplify:
+        try:
+            cuda = torch.cuda.is_available()
+            check_requirements(
+                (
+                    "onnxruntime-gpu" if cuda else "onnxruntime",
+                    "onnx-simplifier>=0.4.1",
+                )
+            )
+            import onnxsim
+
+            LOGGER.info(
+                f"{prefix} simplifying with onnx-simplifier {onnxsim.__version__}..."
+            )
+            model_onnx, check = onnxsim.simplify(model_onnx)
+            assert check, "assert check failed"
+            onnx.save(model_onnx, f)
+        except Exception as e:
+            LOGGER.info(f"{prefix} simplifier failure: {e}")
+    return f, model_onnx
+
+
+@try_export
+def export_openvino(file, metadata, half, prefix=colorstr("OpenVINO:")):
+    # YOLOv5 OpenVINO export
+    check_requirements(
+        "openvino-dev"
+    )  # requires openvino-dev: https://pypi.org/project/openvino-dev/
+    import openvino.inference_engine as ie
+
+    LOGGER.info(
+        f"\n{prefix} starting export with openvino {ie.__version__}..."
+    )
+    f = str(file).replace(".pt", f"_openvino_model{os.sep}")
+
+    cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f} --data_type {'FP16' if half else 'FP32'}"
+    subprocess.run(cmd.split(), check=True, env=os.environ)  # export
+    yaml_save(
+        Path(f) / file.with_suffix(".yaml").name, metadata
+    )  # add metadata.yaml
+    return f, None
+
+
+@try_export
+def export_paddle(model, im, file, metadata, prefix=colorstr("PaddlePaddle:")):
+    # YOLOv5 Paddle export
+    check_requirements(("paddlepaddle", "x2paddle"))
+    import x2paddle
+    from x2paddle.convert import pytorch2paddle
+
+    LOGGER.info(
+        f"\n{prefix} starting export with X2Paddle {x2paddle.__version__}..."
+    )
+    f = str(file).replace(".pt", f"_paddle_model{os.sep}")
+
+    pytorch2paddle(
+        module=model, save_dir=f, jit_type="trace", input_examples=[im]
+    )  # export
+    yaml_save(
+        Path(f) / file.with_suffix(".yaml").name, metadata
+    )  # add metadata.yaml
+    return f, None
+
+
+@try_export
+def export_coreml(model, im, file, int8, half, prefix=colorstr("CoreML:")):
+    # YOLOv5 CoreML export
+    check_requirements("coremltools")
+    import coremltools as ct
+
+    LOGGER.info(
+        f"\n{prefix} starting export with coremltools {ct.__version__}..."
+    )
+    f = file.with_suffix(".mlmodel")
+
+    ts = torch.jit.trace(model, im, strict=False)  # TorchScript model
+    ct_model = ct.convert(
+        ts,
+        inputs=[
+            ct.ImageType(
+                "image", shape=im.shape, scale=1 / 255, bias=[0, 0, 0]
+            )
+        ],
+    )
+    bits, mode = (
+        (8, "kmeans_lut") if int8 else (16, "linear") if half else (32, None)
+    )
+    if bits < 32:
+        if MACOS:  # quantization only supported on macOS
+            with warnings.catch_warnings():
+                warnings.filterwarnings(
+                    "ignore", category=DeprecationWarning
+                )  # suppress numpy==1.20 float warning
+                ct_model = ct.models.neural_network.quantization_utils.quantize_weights(
+                    ct_model, bits, mode
+                )
+        else:
+            print(
+                f"{prefix} quantization only supported on macOS, skipping..."
+            )
+    ct_model.save(f)
+    return f, ct_model
+
+
+@try_export
+def export_engine(
+    model,
+    im,
+    file,
+    half,
+    dynamic,
+    simplify,
+    workspace=4,
+    verbose=False,
+    prefix=colorstr("TensorRT:"),
+):
+    # YOLOv5 TensorRT export https://developer.nvidia.com/tensorrt
+    assert (
+        im.device.type != "cpu"
+    ), "export running on CPU but must be on GPU, i.e. `python export.py --device 0`"
+    try:
+        import tensorrt as trt
+    except Exception:
+        if platform.system() == "Linux":
+            check_requirements(
+                "nvidia-tensorrt",
+                cmds="-U --index-url https://pypi.ngc.nvidia.com",
+            )
+        import tensorrt as trt
+
+    if (
+        trt.__version__[0] == "7"
+    ):  # TensorRT 7 handling https://github.com/ultralytics/yolov5/issues/6012
+        grid = model.model[-1].anchor_grid
+        model.model[-1].anchor_grid = [a[..., :1, :1, :] for a in grid]
+        export_onnx(model, im, file, 12, dynamic, simplify)  # opset 12
+        model.model[-1].anchor_grid = grid
+    else:  # TensorRT >= 8
+        check_version(
+            trt.__version__, "8.0.0", hard=True
+        )  # require tensorrt>=8.0.0
+        export_onnx(model, im, file, 12, dynamic, simplify)  # opset 12
+    onnx = file.with_suffix(".onnx")
+
+    LOGGER.info(
+        f"\n{prefix} starting export with TensorRT {trt.__version__}..."
+    )
+    assert onnx.exists(), f"failed to export ONNX file: {onnx}"
+    f = file.with_suffix(".engine")  # TensorRT engine file
+    logger = trt.Logger(trt.Logger.INFO)
+    if verbose:
+        logger.min_severity = trt.Logger.Severity.VERBOSE
+
+    builder = trt.Builder(logger)
+    config = builder.create_builder_config()
+    config.max_workspace_size = workspace * 1 << 30
+    # config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30)  # fix TRT 8.4 deprecation notice
+
+    flag = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
+    network = builder.create_network(flag)
+    parser = trt.OnnxParser(network, logger)
+    if not parser.parse_from_file(str(onnx)):
+        raise RuntimeError(f"failed to load ONNX file: {onnx}")
+
+    inputs = [network.get_input(i) for i in range(network.num_inputs)]
+    outputs = [network.get_output(i) for i in range(network.num_outputs)]
+    for inp in inputs:
+        LOGGER.info(
+            f'{prefix} input "{inp.name}" with shape{inp.shape} {inp.dtype}'
+        )
+    for out in outputs:
+        LOGGER.info(
+            f'{prefix} output "{out.name}" with shape{out.shape} {out.dtype}'
+        )
+
+    if dynamic:
+        if im.shape[0] <= 1:
+            LOGGER.warning(
+                f"{prefix} WARNING ⚠️ --dynamic model requires maximum --batch-size argument"
+            )
+        profile = builder.create_optimization_profile()
+        for inp in inputs:
+            profile.set_shape(
+                inp.name,
+                (1, *im.shape[1:]),
+                (max(1, im.shape[0] // 2), *im.shape[1:]),
+                im.shape,
+            )
+        config.add_optimization_profile(profile)
+
+    LOGGER.info(
+        f"{prefix} building FP{16 if builder.platform_has_fast_fp16 and half else 32} engine as {f}"
+    )
+    if builder.platform_has_fast_fp16 and half:
+        config.set_flag(trt.BuilderFlag.FP16)
+    with builder.build_engine(network, config) as engine, open(f, "wb") as t:
+        t.write(engine.serialize())
+    return f, None
+
+
+@try_export
+def export_saved_model(
+    model,
+    im,
+    file,
+    dynamic,
+    tf_nms=False,
+    agnostic_nms=False,
+    topk_per_class=100,
+    topk_all=100,
+    iou_thres=0.45,
+    conf_thres=0.25,
+    keras=False,
+    prefix=colorstr("TensorFlow SavedModel:"),
+):
+    # YOLOv5 TensorFlow SavedModel export
+    try:
+        import tensorflow as tf
+    except Exception:
+        check_requirements(
+            f"tensorflow{'' if torch.cuda.is_available() else '-macos' if MACOS else '-cpu'}"
+        )
+        import tensorflow as tf
+    from tensorflow.python.framework.convert_to_constants import (
+        convert_variables_to_constants_v2,
+    )
+
+    from models.tf import TFModel
+
+    LOGGER.info(
+        f"\n{prefix} starting export with tensorflow {tf.__version__}..."
+    )
+    f = str(file).replace(".pt", "_saved_model")
+    batch_size, ch, *imgsz = list(im.shape)  # BCHW
+
+    tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
+    im = tf.zeros((batch_size, *imgsz, ch))  # BHWC order for TensorFlow
+    _ = tf_model.predict(
+        im,
+        tf_nms,
+        agnostic_nms,
+        topk_per_class,
+        topk_all,
+        iou_thres,
+        conf_thres,
+    )
+    inputs = tf.keras.Input(
+        shape=(*imgsz, ch), batch_size=None if dynamic else batch_size
+    )
+    outputs = tf_model.predict(
+        inputs,
+        tf_nms,
+        agnostic_nms,
+        topk_per_class,
+        topk_all,
+        iou_thres,
+        conf_thres,
+    )
+    keras_model = tf.keras.Model(inputs=inputs, outputs=outputs)
+    keras_model.trainable = False
+    keras_model.summary()
+    if keras:
+        keras_model.save(f, save_format="tf")
+    else:
+        spec = tf.TensorSpec(
+            keras_model.inputs[0].shape, keras_model.inputs[0].dtype
+        )
+        m = tf.function(lambda x: keras_model(x))  # full model
+        m = m.get_concrete_function(spec)
+        frozen_func = convert_variables_to_constants_v2(m)
+        tfm = tf.Module()
+        tfm.__call__ = tf.function(
+            lambda x: frozen_func(x)[:4] if tf_nms else frozen_func(x), [spec]
+        )
+        tfm.__call__(im)
+        tf.saved_model.save(
+            tfm,
+            f,
+            options=tf.saved_model.SaveOptions(
+                experimental_custom_gradients=False
+            )
+            if check_version(tf.__version__, "2.6")
+            else tf.saved_model.SaveOptions(),
+        )
+    return f, keras_model
+
+
+@try_export
+def export_pb(keras_model, file, prefix=colorstr("TensorFlow GraphDef:")):
+    # YOLOv5 TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow
+    import tensorflow as tf
+    from tensorflow.python.framework.convert_to_constants import (
+        convert_variables_to_constants_v2,
+    )
+
+    LOGGER.info(
+        f"\n{prefix} starting export with tensorflow {tf.__version__}..."
+    )
+    f = file.with_suffix(".pb")
+
+    m = tf.function(lambda x: keras_model(x))  # full model
+    m = m.get_concrete_function(
+        tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)
+    )
+    frozen_func = convert_variables_to_constants_v2(m)
+    frozen_func.graph.as_graph_def()
+    tf.io.write_graph(
+        graph_or_graph_def=frozen_func.graph,
+        logdir=str(f.parent),
+        name=f.name,
+        as_text=False,
+    )
+    return f, None
+
+
+@try_export
+def export_tflite(
+    keras_model,
+    im,
+    file,
+    int8,
+    data,
+    nms,
+    agnostic_nms,
+    prefix=colorstr("TensorFlow Lite:"),
+):
+    # YOLOv5 TensorFlow Lite export
+    import tensorflow as tf
+
+    LOGGER.info(
+        f"\n{prefix} starting export with tensorflow {tf.__version__}..."
+    )
+    batch_size, ch, *imgsz = list(im.shape)  # BCHW
+    f = str(file).replace(".pt", "-fp16.tflite")
+
+    converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
+    converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]
+    converter.target_spec.supported_types = [tf.float16]
+    converter.optimizations = [tf.lite.Optimize.DEFAULT]
+    if int8:
+        from models.tf import representative_dataset_gen
+
+        dataset = LoadImages(
+            check_dataset(check_yaml(data))["train"],
+            img_size=imgsz,
+            auto=False,
+        )
+        converter.representative_dataset = lambda: representative_dataset_gen(
+            dataset, ncalib=100
+        )
+        converter.target_spec.supported_ops = [
+            tf.lite.OpsSet.TFLITE_BUILTINS_INT8
+        ]
+        converter.target_spec.supported_types = []
+        converter.inference_input_type = tf.uint8  # or tf.int8
+        converter.inference_output_type = tf.uint8  # or tf.int8
+        converter.experimental_new_quantizer = True
+        f = str(file).replace(".pt", "-int8.tflite")
+    if nms or agnostic_nms:
+        converter.target_spec.supported_ops.append(
+            tf.lite.OpsSet.SELECT_TF_OPS
+        )
+
+    tflite_model = converter.convert()
+    open(f, "wb").write(tflite_model)
+    return f, None
+
+
+@try_export
+def export_edgetpu(file, prefix=colorstr("Edge TPU:")):
+    # YOLOv5 Edge TPU export https://coral.ai/docs/edgetpu/models-intro/
+    cmd = "edgetpu_compiler --version"
+    help_url = "https://coral.ai/docs/edgetpu/compiler/"
+    assert (
+        platform.system() == "Linux"
+    ), f"export only supported on Linux. See {help_url}"
+    if subprocess.run(f"{cmd} >/dev/null", shell=True).returncode != 0:
+        LOGGER.info(
+            f"\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}"
+        )
+        sudo = (
+            subprocess.run("sudo --version >/dev/null", shell=True).returncode
+            == 0
+        )  # sudo installed on system
+        for c in (
+            "curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -",
+            'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list',
+            "sudo apt-get update",
+            "sudo apt-get install edgetpu-compiler",
+        ):
+            subprocess.run(
+                c if sudo else c.replace("sudo ", ""), shell=True, check=True
+            )
+    ver = (
+        subprocess.run(cmd, shell=True, capture_output=True, check=True)
+        .stdout.decode()
+        .split()[-1]
+    )
+
+    LOGGER.info(f"\n{prefix} starting export with Edge TPU compiler {ver}...")
+    f = str(file).replace(".pt", "-int8_edgetpu.tflite")  # Edge TPU model
+    f_tfl = str(file).replace(".pt", "-int8.tflite")  # TFLite model
+
+    cmd = f"edgetpu_compiler -s -d -k 10 --out_dir {file.parent} {f_tfl}"
+    subprocess.run(cmd.split(), check=True)
+    return f, None
+
+
+@try_export
+def export_tfjs(file, prefix=colorstr("TensorFlow.js:")):
+    # YOLOv5 TensorFlow.js export
+    check_requirements("tensorflowjs")
+    import tensorflowjs as tfjs
+
+    LOGGER.info(
+        f"\n{prefix} starting export with tensorflowjs {tfjs.__version__}..."
+    )
+    f = str(file).replace(".pt", "_web_model")  # js dir
+    f_pb = file.with_suffix(".pb")  # *.pb path
+    f_json = f"{f}/model.json"  # *.json path
+
+    cmd = (
+        f"tensorflowjs_converter --input_format=tf_frozen_model "
+        f"--output_node_names=Identity,Identity_1,Identity_2,Identity_3 {f_pb} {f}"
+    )
+    subprocess.run(cmd.split())
+
+    json = Path(f_json).read_text()
+    with open(f_json, "w") as j:  # sort JSON Identity_* in ascending order
+        subst = re.sub(
+            r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, '
+            r'"Identity.?.?": {"name": "Identity.?.?"}, '
+            r'"Identity.?.?": {"name": "Identity.?.?"}, '
+            r'"Identity.?.?": {"name": "Identity.?.?"}}}',
+            r'{"outputs": {"Identity": {"name": "Identity"}, '
+            r'"Identity_1": {"name": "Identity_1"}, '
+            r'"Identity_2": {"name": "Identity_2"}, '
+            r'"Identity_3": {"name": "Identity_3"}}}',
+            json,
+        )
+        j.write(subst)
+    return f, None
+
+
+def add_tflite_metadata(file, metadata, num_outputs):
+    # Add metadata to *.tflite models per https://www.tensorflow.org/lite/models/convert/metadata
+    with contextlib.suppress(ImportError):
+        # check_requirements('tflite_support')
+        from tflite_support import flatbuffers
+        from tflite_support import metadata as _metadata
+        from tflite_support import metadata_schema_py_generated as _metadata_fb
+
+        tmp_file = Path("/tmp/meta.txt")
+        with open(tmp_file, "w") as meta_f:
+            meta_f.write(str(metadata))
+
+        model_meta = _metadata_fb.ModelMetadataT()
+        label_file = _metadata_fb.AssociatedFileT()
+        label_file.name = tmp_file.name
+        model_meta.associatedFiles = [label_file]
+
+        subgraph = _metadata_fb.SubGraphMetadataT()
+        subgraph.inputTensorMetadata = [_metadata_fb.TensorMetadataT()]
+        subgraph.outputTensorMetadata = [
+            _metadata_fb.TensorMetadataT()
+        ] * num_outputs
+        model_meta.subgraphMetadata = [subgraph]
+
+        b = flatbuffers.Builder(0)
+        b.Finish(
+            model_meta.Pack(b),
+            _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER,
+        )
+        metadata_buf = b.Output()
+
+        populator = _metadata.MetadataPopulator.with_model_file(file)
+        populator.load_metadata_buffer(metadata_buf)
+        populator.load_associated_files([str(tmp_file)])
+        populator.populate()
+        tmp_file.unlink()
+
+
+@smart_inference_mode()
+def run(
+    data=ROOT / "data/coco128.yaml",  # 'dataset.yaml path'
+    weights=ROOT / "yolov5s.pt",  # weights path
+    imgsz=(640, 640),  # image (height, width)
+    batch_size=1,  # batch size
+    device="cpu",  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+    include=("torchscript", "onnx"),  # include formats
+    half=False,  # FP16 half-precision export
+    inplace=False,  # set YOLOv5 Detect() inplace=True
+    keras=False,  # use Keras
+    optimize=False,  # TorchScript: optimize for mobile
+    int8=False,  # CoreML/TF INT8 quantization
+    dynamic=False,  # ONNX/TF/TensorRT: dynamic axes
+    simplify=False,  # ONNX: simplify model
+    opset=12,  # ONNX: opset version
+    verbose=False,  # TensorRT: verbose log
+    workspace=4,  # TensorRT: workspace size (GB)
+    nms=False,  # TF: add NMS to model
+    agnostic_nms=False,  # TF: add agnostic NMS to model
+    topk_per_class=100,  # TF.js NMS: topk per class to keep
+    topk_all=100,  # TF.js NMS: topk for all classes to keep
+    iou_thres=0.45,  # TF.js NMS: IoU threshold
+    conf_thres=0.25,  # TF.js NMS: confidence threshold
+):
+    t = time.time()
+    include = [x.lower() for x in include]  # to lowercase
+    fmts = tuple(export_formats()["Argument"][1:])  # --include arguments
+    flags = [x in include for x in fmts]
+    assert sum(flags) == len(
+        include
+    ), f"ERROR: Invalid --include {include}, valid --include arguments are {fmts}"
+    (
+        jit,
+        onnx,
+        xml,
+        engine,
+        coreml,
+        saved_model,
+        pb,
+        tflite,
+        edgetpu,
+        tfjs,
+        paddle,
+    ) = flags  # export booleans
+    file = Path(
+        url2file(weights)
+        if str(weights).startswith(("http:/", "https:/"))
+        else weights
+    )  # PyTorch weights
+
+    # Load PyTorch model
+    device = select_device(device)
+    if half:
+        assert (
+            device.type != "cpu" or coreml
+        ), "--half only compatible with GPU export, i.e. use --device 0"
+        assert (
+            not dynamic
+        ), "--half not compatible with --dynamic, i.e. use either --half or --dynamic but not both"
+    model = attempt_load(
+        weights, device=device, inplace=True, fuse=True
+    )  # load FP32 model
+
+    # Checks
+    imgsz *= 2 if len(imgsz) == 1 else 1  # expand
+    if optimize:
+        assert (
+            device.type == "cpu"
+        ), "--optimize not compatible with cuda devices, i.e. use --device cpu"
+
+    # Input
+    gs = int(max(model.stride))  # grid size (max stride)
+    imgsz = [
+        check_img_size(x, gs) for x in imgsz
+    ]  # verify img_size are gs-multiples
+    im = torch.zeros(batch_size, 3, *imgsz).to(
+        device
+    )  # image size(1,3,320,192) BCHW iDetection
+
+    # Update model
+    model.eval()
+    for k, m in model.named_modules():
+        if isinstance(m, Detect):
+            m.inplace = inplace
+            m.dynamic = dynamic
+            m.export = True
+
+    for _ in range(2):
+        y = model(im)  # dry runs
+    if half and not coreml:
+        im, model = im.half(), model.half()  # to FP16
+    shape = tuple(
+        (y[0] if isinstance(y, tuple) else y).shape
+    )  # model output shape
+    metadata = {
+        "stride": int(max(model.stride)),
+        "names": model.names,
+    }  # model metadata
+    LOGGER.info(
+        f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)"
+    )
+
+    # Exports
+    f = [""] * len(fmts)  # exported filenames
+    warnings.filterwarnings(
+        action="ignore", category=torch.jit.TracerWarning
+    )  # suppress TracerWarning
+    if jit:  # TorchScript
+        f[0], _ = export_torchscript(model, im, file, optimize)
+    if engine:  # TensorRT required before ONNX
+        f[1], _ = export_engine(
+            model, im, file, half, dynamic, simplify, workspace, verbose
+        )
+    if onnx or xml:  # OpenVINO requires ONNX
+        f[2], _ = export_onnx(model, im, file, opset, dynamic, simplify)
+    if xml:  # OpenVINO
+        f[3], _ = export_openvino(file, metadata, half)
+    if coreml:  # CoreML
+        f[4], _ = export_coreml(model, im, file, int8, half)
+    if any((saved_model, pb, tflite, edgetpu, tfjs)):  # TensorFlow formats
+        assert (
+            not tflite or not tfjs
+        ), "TFLite and TF.js models must be exported separately, please pass only one type."
+        assert not isinstance(
+            model, ClassificationModel
+        ), "ClassificationModel export to TF formats not yet supported."
+        f[5], s_model = export_saved_model(
+            model.cpu(),
+            im,
+            file,
+            dynamic,
+            tf_nms=nms or agnostic_nms or tfjs,
+            agnostic_nms=agnostic_nms or tfjs,
+            topk_per_class=topk_per_class,
+            topk_all=topk_all,
+            iou_thres=iou_thres,
+            conf_thres=conf_thres,
+            keras=keras,
+        )
+        if pb or tfjs:  # pb prerequisite to tfjs
+            f[6], _ = export_pb(s_model, file)
+        if tflite or edgetpu:
+            f[7], _ = export_tflite(
+                s_model,
+                im,
+                file,
+                int8 or edgetpu,
+                data=data,
+                nms=nms,
+                agnostic_nms=agnostic_nms,
+            )
+            if edgetpu:
+                f[8], _ = export_edgetpu(file)
+            add_tflite_metadata(
+                f[8] or f[7], metadata, num_outputs=len(s_model.outputs)
+            )
+        if tfjs:
+            f[9], _ = export_tfjs(file)
+    if paddle:  # PaddlePaddle
+        f[10], _ = export_paddle(model, im, file, metadata)
+
+    # Finish
+    f = [str(x) for x in f if x]  # filter out '' and None
+    if any(f):
+        cls, det, seg = (
+            isinstance(model, x)
+            for x in (ClassificationModel, DetectionModel, SegmentationModel)
+        )  # type
+        det &= (
+            not seg
+        )  # segmentation models inherit from SegmentationModel(DetectionModel)
+        dir = Path("segment" if seg else "classify" if cls else "")
+        h = "--half" if half else ""  # --half FP16 inference arg
+        s = (
+            "# WARNING ⚠️ ClassificationModel not yet supported for PyTorch Hub AutoShape inference"
+            if cls
+            else "# WARNING ⚠️ SegmentationModel not yet supported for PyTorch Hub AutoShape inference"
+            if seg
+            else ""
+        )
+        LOGGER.info(
+            f"\nExport complete ({time.time() - t:.1f}s)"
+            f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
+            f"\nDetect:          python {dir / ('detect.py' if det else 'predict.py')} --weights {f[-1]} {h}"
+            f"\nValidate:        python {dir / 'val.py'} --weights {f[-1]} {h}"
+            f"\nPyTorch Hub:     model = torch.hub.load('ultralytics/yolov5', 'custom', '{f[-1]}')  {s}"
+            f"\nVisualize:       https://netron.app"
+        )
+    return f  # return list of exported files/dirs
+
+
+def parse_opt():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--data",
+        type=str,
+        default=ROOT / "data/coco128.yaml",
+        help="dataset.yaml path",
+    )
+    parser.add_argument(
+        "--weights",
+        nargs="+",
+        type=str,
+        default=ROOT / "yolov5s.pt",
+        help="model.pt path(s)",
+    )
+    parser.add_argument(
+        "--imgsz",
+        "--img",
+        "--img-size",
+        nargs="+",
+        type=int,
+        default=[640, 640],
+        help="image (h, w)",
+    )
+    parser.add_argument("--batch-size", type=int, default=1, help="batch size")
+    parser.add_argument(
+        "--device", default="cpu", help="cuda device, i.e. 0 or 0,1,2,3 or cpu"
+    )
+    parser.add_argument(
+        "--half", action="store_true", help="FP16 half-precision export"
+    )
+    parser.add_argument(
+        "--inplace",
+        action="store_true",
+        help="set YOLOv5 Detect() inplace=True",
+    )
+    parser.add_argument("--keras", action="store_true", help="TF: use Keras")
+    parser.add_argument(
+        "--optimize",
+        action="store_true",
+        help="TorchScript: optimize for mobile",
+    )
+    parser.add_argument(
+        "--int8", action="store_true", help="CoreML/TF INT8 quantization"
+    )
+    parser.add_argument(
+        "--dynamic", action="store_true", help="ONNX/TF/TensorRT: dynamic axes"
+    )
+    parser.add_argument(
+        "--simplify", action="store_true", help="ONNX: simplify model"
+    )
+    parser.add_argument(
+        "--opset", type=int, default=17, help="ONNX: opset version"
+    )
+    parser.add_argument(
+        "--verbose", action="store_true", help="TensorRT: verbose log"
+    )
+    parser.add_argument(
+        "--workspace",
+        type=int,
+        default=4,
+        help="TensorRT: workspace size (GB)",
+    )
+    parser.add_argument(
+        "--nms", action="store_true", help="TF: add NMS to model"
+    )
+    parser.add_argument(
+        "--agnostic-nms",
+        action="store_true",
+        help="TF: add agnostic NMS to model",
+    )
+    parser.add_argument(
+        "--topk-per-class",
+        type=int,
+        default=100,
+        help="TF.js NMS: topk per class to keep",
+    )
+    parser.add_argument(
+        "--topk-all",
+        type=int,
+        default=100,
+        help="TF.js NMS: topk for all classes to keep",
+    )
+    parser.add_argument(
+        "--iou-thres",
+        type=float,
+        default=0.45,
+        help="TF.js NMS: IoU threshold",
+    )
+    parser.add_argument(
+        "--conf-thres",
+        type=float,
+        default=0.25,
+        help="TF.js NMS: confidence threshold",
+    )
+    parser.add_argument(
+        "--include",
+        nargs="+",
+        default=["torchscript"],
+        help="torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle",
+    )
+    opt = parser.parse_args()
+    print_args(vars(opt))
+    return opt
+
+
+def main(opt):
+    for opt.weights in (
+        opt.weights if isinstance(opt.weights, list) else [opt.weights]
+    ):
+        run(**vars(opt))
+
+
+if __name__ == "__main__":
+    opt = parse_opt()
+    main(opt)

hubconf.py

@@ -0,0 +1,309 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5
+
+Usage:
+    import torch
+    model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # official model
+    model = torch.hub.load('ultralytics/yolov5:master', 'yolov5s')  # from branch
+    model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt')  # custom/local model
+    model = torch.hub.load('.', 'custom', 'yolov5s.pt', source='local')  # local repo
+"""
+
+import torch
+
+
+def _create(
+    name,
+    pretrained=True,
+    channels=3,
+    classes=80,
+    autoshape=True,
+    verbose=True,
+    device=None,
+):
+    """Creates or loads a YOLOv5 model
+
+    Arguments:
+        name (str): model name 'yolov5s' or path 'path/to/best.pt'
+        pretrained (bool): load pretrained weights into the model
+        channels (int): number of input channels
+        classes (int): number of model classes
+        autoshape (bool): apply YOLOv5 .autoshape() wrapper to model
+        verbose (bool): print all information to screen
+        device (str, torch.device, None): device to use for model parameters
+
+    Returns:
+        YOLOv5 model
+    """
+    from pathlib import Path
+
+    from models.common import AutoShape, DetectMultiBackend
+    from models.experimental import attempt_load
+    from models.yolo import ClassificationModel, DetectionModel, SegmentationModel
+    from utils.downloads import attempt_download
+    from utils.general import LOGGER, check_requirements, intersect_dicts, logging
+    from utils.torch_utils import select_device
+
+    if not verbose:
+        LOGGER.setLevel(logging.WARNING)
+    check_requirements(exclude=("opencv-python", "tensorboard", "thop"))
+    name = Path(name)
+    path = (
+        name.with_suffix(".pt")
+        if name.suffix == "" and not name.is_dir()
+        else name
+    )  # checkpoint path
+    try:
+        device = select_device(device)
+        if pretrained and channels == 3 and classes == 80:
+            try:
+                model = DetectMultiBackend(
+                    path, device=device, fuse=autoshape
+                )  # detection model
+                if autoshape:
+                    if model.pt and isinstance(
+                        model.model, ClassificationModel
+                    ):
+                        LOGGER.warning(
+                            "WARNING ⚠️ YOLOv5 ClassificationModel is not yet AutoShape compatible. "
+                            "You must pass torch tensors in BCHW to this model, i.e. shape(1,3,224,224)."
+                        )
+                    elif model.pt and isinstance(
+                        model.model, SegmentationModel
+                    ):
+                        LOGGER.warning(
+                            "WARNING ⚠️ YOLOv5 SegmentationModel is not yet AutoShape compatible. "
+                            "You will not be able to run inference with this model."
+                        )
+                    else:
+                        model = AutoShape(
+                            model
+                        )  # for file/URI/PIL/cv2/np inputs and NMS
+            except Exception:
+                model = attempt_load(
+                    path, device=device, fuse=False
+                )  # arbitrary model
+        else:
+            cfg = list(
+                (Path(__file__).parent / "models").rglob(f"{path.stem}.yaml")
+            )[
+                0
+            ]  # model.yaml path
+            model = DetectionModel(cfg, channels, classes)  # create model
+            if pretrained:
+                ckpt = torch.load(
+                    attempt_download(path), map_location=device
+                )  # load
+                csd = (
+                    ckpt["model"].float().state_dict()
+                )  # checkpoint state_dict as FP32
+                csd = intersect_dicts(
+                    csd, model.state_dict(), exclude=["anchors"]
+                )  # intersect
+                model.load_state_dict(csd, strict=False)  # load
+                if len(ckpt["model"].names) == classes:
+                    model.names = ckpt[
+                        "model"
+                    ].names  # set class names attribute
+        if not verbose:
+            LOGGER.setLevel(logging.INFO)  # reset to default
+        return model.to(device)
+
+    except Exception as e:
+        help_url = "https://github.com/ultralytics/yolov5/issues/36"
+        s = f"{e}. Cache may be out of date, try `force_reload=True` or see {help_url} for help."
+        raise Exception(s) from e
+
+
+def custom(
+    path="path/to/model.pt", autoshape=True, _verbose=True, device=None
+):
+    # YOLOv5 custom or local model
+    return _create(path, autoshape=autoshape, verbose=_verbose, device=device)
+
+
+def yolov5n(
+    pretrained=True,
+    channels=3,
+    classes=80,
+    autoshape=True,
+    _verbose=True,
+    device=None,
+):
+    # YOLOv5-nano model https://github.com/ultralytics/yolov5
+    return _create(
+        "yolov5n", pretrained, channels, classes, autoshape, _verbose, device
+    )
+
+
+def yolov5s(
+    pretrained=True,
+    channels=3,
+    classes=80,
+    autoshape=True,
+    _verbose=True,
+    device=None,
+):
+    # YOLOv5-small model https://github.com/ultralytics/yolov5
+    return _create(
+        "yolov5s", pretrained, channels, classes, autoshape, _verbose, device
+    )
+
+
+def yolov5m(
+    pretrained=True,
+    channels=3,
+    classes=80,
+    autoshape=True,
+    _verbose=True,
+    device=None,
+):
+    # YOLOv5-medium model https://github.com/ultralytics/yolov5
+    return _create(
+        "yolov5m", pretrained, channels, classes, autoshape, _verbose, device
+    )
+
+
+def yolov5l(
+    pretrained=True,
+    channels=3,
+    classes=80,
+    autoshape=True,
+    _verbose=True,
+    device=None,
+):
+    # YOLOv5-large model https://github.com/ultralytics/yolov5
+    return _create(
+        "yolov5l", pretrained, channels, classes, autoshape, _verbose, device
+    )
+
+
+def yolov5x(
+    pretrained=True,
+    channels=3,
+    classes=80,
+    autoshape=True,
+    _verbose=True,
+    device=None,
+):
+    # YOLOv5-xlarge model https://github.com/ultralytics/yolov5
+    return _create(
+        "yolov5x", pretrained, channels, classes, autoshape, _verbose, device
+    )
+
+
+def yolov5n6(
+    pretrained=True,
+    channels=3,
+    classes=80,
+    autoshape=True,
+    _verbose=True,
+    device=None,
+):
+    # YOLOv5-nano-P6 model https://github.com/ultralytics/yolov5
+    return _create(
+        "yolov5n6", pretrained, channels, classes, autoshape, _verbose, device
+    )
+
+
+def yolov5s6(
+    pretrained=True,
+    channels=3,
+    classes=80,
+    autoshape=True,
+    _verbose=True,
+    device=None,
+):
+    # YOLOv5-small-P6 model https://github.com/ultralytics/yolov5
+    return _create(
+        "yolov5s6", pretrained, channels, classes, autoshape, _verbose, device
+    )
+
+
+def yolov5m6(
+    pretrained=True,
+    channels=3,
+    classes=80,
+    autoshape=True,
+    _verbose=True,
+    device=None,
+):
+    # YOLOv5-medium-P6 model https://github.com/ultralytics/yolov5
+    return _create(
+        "yolov5m6", pretrained, channels, classes, autoshape, _verbose, device
+    )
+
+
+def yolov5l6(
+    pretrained=True,
+    channels=3,
+    classes=80,
+    autoshape=True,
+    _verbose=True,
+    device=None,
+):
+    # YOLOv5-large-P6 model https://github.com/ultralytics/yolov5
+    return _create(
+        "yolov5l6", pretrained, channels, classes, autoshape, _verbose, device
+    )
+
+
+def yolov5x6(
+    pretrained=True,
+    channels=3,
+    classes=80,
+    autoshape=True,
+    _verbose=True,
+    device=None,
+):
+    # YOLOv5-xlarge-P6 model https://github.com/ultralytics/yolov5
+    return _create(
+        "yolov5x6", pretrained, channels, classes, autoshape, _verbose, device
+    )
+
+
+if __name__ == "__main__":
+    import argparse
+    from pathlib import Path
+
+    import numpy as np
+    from PIL import Image
+
+    from utils.general import cv2, print_args
+
+    # Argparser
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model", type=str, default="yolov5s", help="model name"
+    )
+    opt = parser.parse_args()
+    print_args(vars(opt))
+
+    # Model
+    model = _create(
+        name=opt.model,
+        pretrained=True,
+        channels=3,
+        classes=80,
+        autoshape=True,
+        verbose=True,
+    )
+    # model = custom(path='path/to/model.pt')  # custom
+
+    # Images
+    imgs = [
+        "data/images/zidane.jpg",  # filename
+        Path("data/images/zidane.jpg"),  # Path
+        "https://ultralytics.com/images/zidane.jpg",  # URI
+        cv2.imread("data/images/bus.jpg")[:, :, ::-1],  # OpenCV
+        Image.open("data/images/bus.jpg"),  # PIL
+        np.zeros((320, 640, 3)),
+    ]  # numpy
+
+    # Inference
+    results = model(imgs, size=320)  # batched inference
+
+    # Results
+    results.print()
+    results.save()

inference.py

@@ -0,0 +1,226 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
+
+Usage - sources:
+    $ python detect.py --weights yolov5s.pt --source 0                               # webcam
+                                                     img.jpg                         # image
+                                                     vid.mp4                         # video
+                                                     screen                          # screenshot
+                                                     path/                           # directory
+                                                     list.txt                        # list of images
+                                                     list.streams                    # list of streams
+                                                     'path/*.jpg'                    # glob
+                                                     'https://youtu.be/Zgi9g1ksQHc'  # YouTube
+                                                     'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
+
+Usage - formats:
+    $ python detect.py --weights yolov5s.pt                 # PyTorch
+                                 yolov5s.torchscript        # TorchScript
+                                 yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
+                                 yolov5s_openvino_model     # OpenVINO
+                                 yolov5s.engine             # TensorRT
+                                 yolov5s.mlmodel            # CoreML (macOS-only)
+                                 yolov5s_saved_model        # TensorFlow SavedModel
+                                 yolov5s.pb                 # TensorFlow GraphDef
+                                 yolov5s.tflite             # TensorFlow Lite
+                                 yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
+                                 yolov5s_paddle_model       # PaddlePaddle
+"""
+
+import argparse
+import os
+import platform
+import sys
+from pathlib import Path
+
+import torch
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[0]  # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
+
+from models.common import DetectMultiBackend
+from utils.dataloaders import (
+    IMG_FORMATS,
+    VID_FORMATS,
+    LoadImages,
+    LoadScreenshots,
+    LoadStreams,
+)
+from utils.general import (
+    LOGGER,
+    Profile,
+    check_file,
+    check_img_size,
+    check_imshow,
+    check_requirements,
+    colorstr,
+    cv2,
+    increment_path,
+    non_max_suppression,
+    print_args,
+    scale_boxes,
+    strip_optimizer,
+    xyxy2xywh,
+)
+from utils.plots import Annotator, colors, save_one_box
+from utils.torch_utils import select_device, smart_inference_mode
+
+
+@smart_inference_mode()
+def run(
+    weights=ROOT / "yolov5s.pt",  # model path or triton URL
+    source=ROOT / "data/images",  # file/dir/URL/glob/screen/0(webcam)
+    data=ROOT / "data/coco128.yaml",  # dataset.yaml path
+    imgsz=(640, 640),  # inference size (height, width)
+    conf_thres=0.25,  # confidence threshold
+    iou_thres=0.45,  # NMS IOU threshold
+    max_det=1000,  # maximum detections per image
+    device="",  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+    view_img=False,  # show results
+    save_txt=False,  # save results to *.txt
+    save_conf=False,  # save confidences in --save-txt labels
+    save_crop=False,  # save cropped prediction boxes
+    nosave=False,  # do not save images/videos
+    classes=None,  # filter by class: --class 0, or --class 0 2 3
+    agnostic_nms=False,  # class-agnostic NMS
+    augment=False,  # augmented inference
+    visualize=False,  # visualize features
+    update=False,  # update all models
+    project=ROOT / "runs/detect",  # save results to project/name
+    name="exp",  # save results to project/name
+    exist_ok=False,  # existing project/name ok, do not increment
+    line_thickness=3,  # bounding box thickness (pixels)
+    hide_labels=False,  # hide labels
+    hide_conf=False,  # hide confidences
+    half=False,  # use FP16 half-precision inference
+    dnn=False,  # use OpenCV DNN for ONNX inference
+    vid_stride=1,  # video frame-rate stride
+):
+    source = str(source)
+    save_img = not nosave and not source.endswith(
+        ".txt"
+    )  # save inference images
+    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
+    is_url = source.lower().startswith(
+        ("rtsp://", "rtmp://", "http://", "https://")
+    )
+    webcam = (
+        source.isnumeric()
+        or source.endswith(".streams")
+        or (is_url and not is_file)
+    )
+    screenshot = source.lower().startswith("screen")
+    if is_url and is_file:
+        source = check_file(source)  # download
+
+    # Directories
+    save_dir = increment_path(
+        Path(project) / name, exist_ok=exist_ok
+    )  # increment run
+    (save_dir / "labels" if save_txt else save_dir).mkdir(
+        parents=True, exist_ok=True
+    )  # make dir
+
+    # Load model
+    device = select_device(device)
+    model = DetectMultiBackend(
+        weights, device=device, dnn=dnn, data=data, fp16=half
+    )
+    stride, names, pt = model.stride, model.names, model.pt
+    imgsz = check_img_size(imgsz, s=stride)  # check image size
+
+    # Dataloader
+    bs = 1  # batch_size
+    if webcam:
+        view_img = check_imshow(warn=True)
+        dataset = LoadStreams(
+            source,
+            img_size=imgsz,
+            stride=stride,
+            auto=pt,
+            vid_stride=vid_stride,
+        )
+        bs = len(dataset)
+    elif screenshot:
+        dataset = LoadScreenshots(
+            source, img_size=imgsz, stride=stride, auto=pt
+        )
+    else:
+        dataset = LoadImages(
+            source,
+            img_size=imgsz,
+            stride=stride,
+            auto=pt,
+            vid_stride=vid_stride,
+        )
+    vid_path, vid_writer = [None] * bs, [None] * bs
+
+    # Run inference
+    model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz))  # warmup
+    seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
+    for path, im, im0s, vid_cap, s in dataset:
+        with dt[0]:
+            im = torch.from_numpy(im).to(model.device)
+            im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
+            im /= 255  # 0 - 255 to 0.0 - 1.0
+            if len(im.shape) == 3:
+                im = im[None]  # expand for batch dim
+
+        # Inference
+        with dt[1]:
+            visualize = (
+                increment_path(save_dir / Path(path).stem, mkdir=True)
+                if visualize
+                else False
+            )
+            pred = model(im, augment=augment, visualize=visualize)
+
+        # NMS
+        with dt[2]:
+            pred = non_max_suppression(
+                pred,
+                conf_thres,
+                iou_thres,
+                classes,
+                agnostic_nms,
+                max_det=max_det,
+            )
+
+        # Second-stage classifier (optional)
+        # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
+
+        # Process predictions
+        for i, det in enumerate(pred):  # per image
+            seen += 1
+            if webcam:  # batch_size >= 1
+                p, im0, frame = path[i], im0s[i].copy(), dataset.count
+                s += f"{i}: "
+            else:
+                p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0)
+
+            p = Path(p)  # to Path
+            save_path = str(save_dir / p.name)  # im.jpg
+            txt_path = str(save_dir / "labels" / p.stem) + (
+                "" if dataset.mode == "image" else f"_{frame}"
+            )  # im.txt
+            s += "%gx%g " % im.shape[2:]  # print string
+            gn = torch.tensor(im0.shape)[
+                [1, 0, 1, 0]
+            ]  # normalization gain whwh
+            imc = im0.copy() if save_crop else im0  # for save_crop
+            annotator = Annotator(
+                im0, line_width=line_thickness, example=str(names)
+            )
+            results = []
+            if len(det):
+                # Rescale boxes from img_size to im0 size
+                det[:, :4] = scale_boxes(
+                    im.shape[2:], det[:, :4], im0.shape
+                ).round()
+                results.append((path, det))
+
+    return results