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---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: string
- name: mask
dtype: image
splits:
- name: train
num_bytes: 10549367.8
num_examples: 16
- name: test
num_bytes: 2560723.2
num_examples: 4
download_size: 12487906
dataset_size: 13110091
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
license: apache-2.0
language:
- en
tags:
- poseestimation
- pose
- estimation
- synthetic
- syntheticdata
size_categories:
- 1K<n<10K
---
# Pose Estimation Dataset
[Duality AI](https://www.duality.ai/edu) released a 1000 image dataset for pose estimation, including images, masks, and labels -- and it's 100% free!
## Just [create an EDU account by clicking here](https://falcon.duality.ai/secure/documentation/pose-colab?sidebarMode=learn&utm_source=huggingface&utm_medium=hf_dataset&utm_campaign=pose_estimation_5-1).
This HuggingFace dataset is a 20 image and label sample divided into train and val folders, but you can get the rest at no cost by creating an EDU account. Once you verify your email, the link will redirect you to the dataset page.
What makes this dataset unique, useful, and capable of bridging the Sim2Real gap?
- The digital twins are not generated by AI, but instead crafted by 3D artists to be INDISTINGUISHABLE to the model from the physical-world objects. This allows the training from this data to transfer into real-world applicability
- The simulation software, called FalconEditor, can easily create thousands of images with varying lighting, posing, occlusions, backgrounds, camera positions, and more. This enables robust model training.
- The labels are created along with the data, and specifically the pose-estimation labels are easier to generate in simulation than in the physical world. This not only saves large amounts of time, but also ensures the labels are incredibly accurate and reliable.
This dataset is designed for pose estimation tasks, focusing on determining the position and orientation of an object in 3D space. The dataset includes images, masks, and labels for both training and validation, making it suitable for machine learning applications in 3D object tracking and computer vision. This dataset was generated using Duality.ai simulation software: FalconEditor. [Try FalconEditor today](https://falcon.duality.ai/secure/documentation/pose-colab?sidebarMode=learn) to create data to be used for pose estimation on different objects.
## Dataset Structure
The dataset has the following structure:
```plaintext
pose_estimation_dataset/
|-- train/
| |-- images/
| | |-- 000000000.png
| | |-- 000000001.png
| | |-- ...
| |-- labels/
| | |-- 000000000.txt
| | |-- 000000001.txt
| | |-- ...
| |-- masks/
| |-- 000000000.png
| |-- 000000001.png
| |-- ...
|-- val/
|-- images/
| |-- 000000000.png
| |-- 000000001.png
| |-- ...
|-- labels/
| |-- 000000000.txt
| |-- 000000001.txt
| |-- ...
|-- masks/
|-- 000000000.png
|-- 000000001.png
|-- ...
```
### Components
1. **Images**: RGB images of the object in `.jpg` format.
2. **Labels**: Text files (`.txt`) containing 3D pose annotations. Each label file corresponds to an image and contains the following information:
- Three position values `[x, y, z]` representing the object's location in 3D space.
- Four quaternion values `[qx, qy, qz, qw]` representing the object's orientation in 3D space.
3. **Masks**: Binary masks (`.jpg`) highlighting the object’s silhouette in the image.
## Usage
To use this dataset, load the images, labels, and masks for your pose estimation pipeline. Ensure that the corresponding image, label, and mask files share the same base filename.
### Example
If you have `train/images/image_1.png`, the corresponding files will be:
- `train/labels/image_1.txt`
- `train/masks/image_1.png`
### Label Format
Each `.txt` label file contains a single line in the following format:
```plaintext
x y z qx qy qz qw
```
Example:
```plaintext
0.12 0.45 0.78 0.0 0.707 0.0 0.707
```
## Licensing
license: apache-2.0 |