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# 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](../../tools/quantize/block_quantize.py) with `block_size=64`.
## Demo
### Python
Run the following command to try the demo:
```shell
# 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:
```shell
# 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](../../benchmark/download_data.py) for the original images.
## Model metrics:
The model is evaluated on [COCO 2017 val](https://cocodataset.org/#download). Results are showed below:
<table>
<tr><th>Average Precision </th><th>Average Recall</th></tr>
<tr><td>
| area | IoU | Average Precision(AP) |
|:-------|:------|:------------------------|
| all | 0.50:0.95 | 0.405 |
| all | 0.50 | 0.593 |
| all | 0.75 | 0.437 |
| small | 0.50:0.95 | 0.232 |
| medium | 0.50:0.95 | 0.448 |
| large | 0.50:0.95 | 0.541 |
</td><td>
| area | IoU | Average Recall(AR) |
|:-------|:------|:----------------|
| all | 0.50:0.95 | 0.326 |
| all | 0.50:0.95 | 0.531 |
| all | 0.50:0.95 | 0.574 |
| small | 0.50:0.95 | 0.365 |
| medium | 0.50:0.95 | 0.634 |
| large | 0.50:0.95 | 0.724 |
</td></tr> </table>
| class | AP | class | AP | class | AP |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person | 54.109 | bicycle | 31.580 | car | 40.447 |
| motorcycle | 43.477 | airplane | 66.070 | bus | 64.183 |
| train | 64.483 | truck | 35.110 | boat | 24.681 |
| traffic light | 25.068 | fire hydrant | 64.382 | stop sign | 65.333 |
| parking meter | 48.439 | bench | 22.653 | bird | 33.324 |
| cat | 66.394 | dog | 60.096 | horse | 58.080 |
| sheep | 49.456 | cow | 53.596 | elephant | 65.574 |
| bear | 70.541 | zebra | 66.461 | giraffe | 66.780 |
| backpack | 13.095 | umbrella | 41.614 | handbag | 12.865 |
| tie | 29.453 | suitcase | 39.089 | frisbee | 61.712 |
| skis | 21.623 | snowboard | 31.326 | sports ball | 39.820 |
| kite | 41.410 | baseball bat | 27.311 | baseball glove | 36.661 |
| skateboard | 49.374 | surfboard | 35.524 | tennis racket | 45.569 |
| bottle | 37.270 | wine glass | 33.088 | cup | 39.835 |
| fork | 31.620 | knife | 15.265 | spoon | 14.918 |
| bowl | 43.251 | banana | 27.904 | apple | 17.630 |
| sandwich | 32.789 | orange | 29.388 | broccoli | 23.187 |
| carrot | 23.114 | hot dog | 33.716 | pizza | 52.541 |
| donut | 47.980 | cake | 36.160 | chair | 29.707 |
| couch | 46.175 | potted plant | 24.781 | bed | 44.323 |
| dining table | 30.022 | toilet | 64.237 | tv | 57.301 |
| laptop | 58.362 | mouse | 57.774 | remote | 24.271 |
| keyboard | 48.020 | cell phone | 32.376 | microwave | 57.220 |
| oven | 36.168 | toaster | 28.735 | sink | 38.159 |
| refrigerator | 52.876 | book | 15.030 | clock | 48.622 |
| vase | 37.013 | scissors | 26.307 | teddy bear | 45.676 |
| hair drier | 7.255 | toothbrush | 19.374 | | |
## License
All files in this directory are licensed under [Apache 2.0 License](./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
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