Update README.md

README.md

@@ -35,94 +35,11 @@ The model was trained on several datasets:
 
 The combined dataset consists of **45,726** unique images. Each image is augmented by being rotated in four ways (0°, 90°, 180°, 270°), creating a total of **182,904** samples. This augmented dataset was then split into **146,323 samples for training** and **36,581 samples for validation**.
 
-## Project Structure
-
-```
-image_orientation_detector/
-├───.gitignore
-├───config.py                 # Main configuration file for paths, model, and hyperparameters
-├───convert_to_onnx.py        # Script to convert the PyTorch model to ONNX format
-├───predict.py                # Script for running inference on new images
-├───README.md                 # This file
-├───requirements.txt          # Python dependencies
-├───train.py                  # Main script for training the model
-├───data/
-│   ├───upright_images/       # Directory for correctly oriented images
-│   └───cache/                # Directory for cached, pre-rotated images (auto-generated)
-├───models/
-│   └───best_model.pth        # The best trained model weights
-└───src/
-    ├───caching.py            # Logic for creating the image cache
-    ├───dataset.py            # PyTorch Dataset classes
-    ├───model.py              # Model definition (EfficientNetV2)
-    └───utils.py              # Utility functions (e.g., device setup, transforms)
-```
-
 ## Usage
 
-### Getting Started
-
-You can download the pre-trained model (`orientation_model_xx.pth`) and its ONNX version (`orientation_model_xx.onnx`) from the [GitHub Releases](https://github.com/your-repo/your-project/releases) page.
-
-Install the required Python packages using the `requirements.txt` file:
-
-```bash
-pip install -r requirements.txt
-```
-
-### Prediction
-
-To predict the orientation of an image or a directory of images, there's a `predict.py` script.
-
-- **Predict a single image:**
-
-  ```bash
-  python predict.py --input_path /path/to/image.jpg
-  ```
-- **Predict all images in a directory:**
-
-  ```bash
-  python predict.py --input_path /path/to/directory/
-  ```
-
-The script will output the predicted orientation for each image.
-
-### ONNX Export and Prediction
-
-This project also includes exporting the trained PyTorch model to the ONNX (Open Neural Network Exchange) format. This allows for faster inference, especially on hardware that doesn't have PyTorch installed.
-
-To convert a `.pth` model to `.onnx`, provide the path to the model file:
-
-```bash
-python convert_to_onnx.py path/to/model.pth
-```
-
-This will create a `model.onnx` file in the same directory.
-
-To predict image orientation using the ONNX model:
-
-- **Predict a single image:**
-
-  ```bash
-  python predict_onnx.py --input_path /path/to/image.jpg
-  ```
-- **Predict all images in a directory:**
-
-  ```bash
-  python predict_onnx.py --input_path /path/to/directory/
-  ```
-
-#### ONNX GPU Acceleration (Optional)
-
-For even better performance on NVIDIA GPUs, you can install the GPU-enabled version of ONNX Runtime.
-
-```bash
-pip install onnxruntime-gpu
-```
+For detailed usage instructions, including how to run predictions, export to ONNX, and train the model, please refer to the [GitHub repository](https://github.com/duartebarbosadev/deep-image-orientation-detection).
 
-Make sure you have a compatible CUDA toolkit installed on your system. The `predict_onnx.py` script will automatically try to use the CUDA provider if it's available.
-
-#### Performance Comparison (PyTorch vs. ONNX)
+## Performance Comparison (PyTorch vs. ONNX)
 
 For a dataset of 5055 images, the performance on a RTX 4080 running in **single-thread** was:
 
@@ -131,67 +48,6 @@ For a dataset of 5055 images, the performance on a RTX 4080 running in **single-
 
 This demonstrates a significant performance gain of approximately **55.2%** when using the ONNX model for inference.
 
-### Training
-
-This model learns to identify image orientation by training on a dataset of images that you provide. For the model to learn effectively, provide images that are correctly oriented.
-
-**Place Images in the `data/upright_images` directory**: All images must be placed in the `data/upright_images` directory. The training script will automatically generate rotated versions (90°, 180°, 270°) of these images and cache them for efficient training.
-
-The directory structure should look like this:
-
-```
-data/
-└───upright_images/
-    ├───image1.jpg
-    ├───image2.png
-    └───...
-```
-
-### Configure the Training
-
-All training parameters are centralized in the `config.py` file. Before starting the training, review and adjust the settings to match the hardware and dataset.
-
-Key configuration options in `config.py`:
-
-- **Paths and Caching**:
-
-  - `TRAIN_IMAGES_PATH`: Path to upright images. Defaults to `data/upright_images`.
-  - `CACHE_PATH`: Directory where rotated images will be cached. Defaults to `data/cache`.
-  - `USE_CACHE`: Set to `True` to use the cache on subsequent runs, significantly speeding up data loading but takes a lot of disk space.
-- **Model and Training Hyperparameters**:
-
-  - `MODEL_NAME`: The name of the model architecture to use (e.g., `EfficientNetV2S`).
-  - `IMAGE_SIZE`: The resolution to which images will be resized (e.g., `224` for 224x224 pixels).
-  - `BATCH_SIZE`: Number of images to process in each batch. Adjust based on GPU's VRAM.
-  - `NUM_EPOCHS`: The total number of times the model will iterate over the entire dataset.
-  - `LEARNING_RATE`: The initial learning rate for the optimizer.
-
-### Start Training
-
-Once all data is in place and the configuration is set,  start training the model by running the `train.py` script:
-
-```bash
-python train.py
-```
-
-- **First Run**: The first time the script runs, it will preprocess and cache the dataset. This may take a while depending on the size of the dataset.
-- **Subsequent Runs**: Later runs will be much faster as they will use the cached data.
-- **Monitoring**: Use TensorBoard to monitor training progress by running `tensorboard --logdir=runs`.
-
-### Monitoring with TensorBoard
-
-The training script is integrated with TensorBoard to help visualize metrics and understand the model's performance. During training, logs are saved in the `runs/` directory.
-
-To launch TensorBoard, run the command:
-
-```bash
-tensorboard --logdir=runs
-```
-
-This will start a web server, open the provided URL (usually `http://localhost:6006`) in the browser to view the dashboard.
-
-In TensorBoard, you can track:
+---
 
-- **Accuracy:** `Accuracy/train` and `Accuracy/validation`
-- **Loss:** `Loss/train` and `Loss/validation`
-- **Learning Rate:** `Hyperparameters/learning_rate` to see how it changes over epochs.
+For more in-depth information about the project, including the full source code, training scripts, and detailed documentation, please visit the [GitHub repository](https://github.com/duartebarbosadev/deep-image-orientation-detection).