object_detection_yolox

YOLOX

Nanodet: YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities. YOLOX is a high-performing object detector, an improvement to the existing YOLO series. YOLO series are in constant exploration of techniques to improve the object detection techniques for optimal speed and accuracy trade-off for real-time applications.

Key features of the YOLOX object detector

• Anchor-free detectors significantly reduce the number of design parameters
• A decoupled head for classification, regression, and localization improves the convergence speed
• SimOTA advanced label assignment strategy reduces training time and avoids additional solver hyperparameters
• Strong data augmentations like MixUp and Mosiac to boost YOLOX performance

Note:

• This version of YoloX: YoloX_s
• object_detection_yolox_2022nov_int8bq.onnx represents the block-quantized version in int8 precision and is generated using block_quantize.py with block_size=64.

Demo

Python

Run the following command to try the demo:

# detect on camera input
python demo.py
# detect on an image
python demo.py --input /path/to/image -v

Note:

• image result saved as "result.jpg"
• this model requires opencv-python>=4.8.0

C++

Install latest OpenCV and CMake >= 3.24.0 to get started with:

# A typical and default installation path of OpenCV is /usr/local
cmake -B build -D OPENCV_INSTALLATION_PATH=/path/to/opencv/installation .
cmake --build build

# detect on camera input
./build/opencv_zoo_object_detection_yolox
# detect on an image
./build/opencv_zoo_object_detection_yolox -m=/path/to/model -i=/path/to/image -v
# get help messages
./build/opencv_zoo_object_detection_yolox -h

Results

Here are some of the sample results that were observed using the model (yolox_s.onnx),

Check benchmark/download_data.py for the original images.

Model metrics:

The model is evaluated on COCO 2017 val. Results are showed below:

<table> <tr><th>Average Precision </th><th>Average Recall</th></tr> <tr><td> </td><td> </td></tr> </table>

License

All files in this directory are licensed under Apache 2.0 License.

Contributor Details

• Google Summer of Code'22
• Contributor: Sri Siddarth Chakaravarthy
• Github Profile: https://github.com/Sidd1609
• Organisation: OpenCV
• Project: Lightweight object detection models using OpenCV

Reference

• YOLOX article: https://arxiv.org/abs/2107.08430
• YOLOX weight and scripts for training: https://github.com/Megvii-BaseDetection/YOLOX
• YOLOX blog: https://arshren.medium.com/yolox-new-improved-yolo-d430c0e4cf20
• YOLOX-lite: https://github.com/TexasInstruments/edgeai-yolox