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add object detection model YoloX
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‎models/object_detection_yolox/LICENSE

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‎models/object_detection_yolox/README.md

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# YOLOX
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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.
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Key features of the YOLOX object detector
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- **Anchor-free detectors** significantly reduce the number of design parameters
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- **A decoupled head for classification, regression, and localization** improves the convergence speed
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- **SimOTA advanced label assignment strategy** reduces training time and avoids additional solver hyperparameters
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- **Strong data augmentations like MixUp and Mosiac** to boost YOLOX performance
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Note:
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- This version of YoloX: YoloX_s
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## Demo
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Run the following command to try the demo:
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```shell
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# detect on camera input
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python demo.py
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# detect on an image
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python demo.py --input /path/to/image
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```
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Note:
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- image result saved as "result.jpg"
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## Results
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Here are some of the sample results that were observed using the model (**yolox_s.onnx**),
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![1_res.jpg](./samples/1_res.jpg)
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![2_res.jpg](./samples/2_res.jpg)
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![3_res.jpg](./samples/3_res.jpg)
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<!--
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Video inference result,
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![WebCamR.gif](./examples/results/WebCamR.gif)
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-->
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## Model metrics:
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The model is evaluated on [COCO 2017 val](https://cocodataset.org/#download). Results are showed below:
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<table>
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<tr><th>Average Precision </th><th>Average Recall</th></tr>
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<tr><td>
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| area | IoU | Average Precision(AP) |
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|:-------|:------|:------------------------|
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| all | 0.50:0.95 | 0.405 |
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| all | 0.50 | 0.593 |
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| all | 0.75 | 0.437 |
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| small | 0.50:0.95 | 0.232 |
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| medium | 0.50:0.95 | 0.448 |
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| large | 0.50:0.95 | 0.541 |
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</td><td>
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area | IoU | Average Recall(AR) |
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|:-------|:------|:----------------|
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| all | 0.50:0.95 | 0.326 |
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| all | 0.50:0.95 | 0.531 |
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| all | 0.50:0.95 | 0.574 |
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| small | 0.50:0.95 | 0.365 |
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| medium | 0.50:0.95 | 0.634 |
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| large | 0.50:0.95 | 0.724 |
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</td></tr> </table>
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| class | AP | class | AP | class | AP |
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|:--------------|:-------|:-------------|:-------|:---------------|:-------|
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| person | 54.109 | bicycle | 31.580 | car | 40.447 |
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| motorcycle | 43.477 | airplane | 66.070 | bus | 64.183 |
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| train | 64.483 | truck | 35.110 | boat | 24.681 |
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| traffic light | 25.068 | fire hydrant | 64.382 | stop sign | 65.333 |
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| parking meter | 48.439 | bench | 22.653 | bird | 33.324 |
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| cat | 66.394 | dog | 60.096 | horse | 58.080 |
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| sheep | 49.456 | cow | 53.596 | elephant | 65.574 |
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| bear | 70.541 | zebra | 66.461 | giraffe | 66.780 |
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| backpack | 13.095 | umbrella | 41.614 | handbag | 12.865 |
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| tie | 29.453 | suitcase | 39.089 | frisbee | 61.712 |
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| skis | 21.623 | snowboard | 31.326 | sports ball | 39.820 |
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| kite | 41.410 | baseball bat | 27.311 | baseball glove | 36.661 |
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| skateboard | 49.374 | surfboard | 35.524 | tennis racket | 45.569 |
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| bottle | 37.270 | wine glass | 33.088 | cup | 39.835 |
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| fork | 31.620 | knife | 15.265 | spoon | 14.918 |
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| bowl | 43.251 | banana | 27.904 | apple | 17.630 |
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| sandwich | 32.789 | orange | 29.388 | broccoli | 23.187 |
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| carrot | 23.114 | hot dog | 33.716 | pizza | 52.541 |
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| donut | 47.980 | cake | 36.160 | chair | 29.707 |
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| couch | 46.175 | potted plant | 24.781 | bed | 44.323 |
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| dining table | 30.022 | toilet | 64.237 | tv | 57.301 |
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| laptop | 58.362 | mouse | 57.774 | remote | 24.271 |
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| keyboard | 48.020 | cell phone | 32.376 | microwave | 57.220 |
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| oven | 36.168 | toaster | 28.735 | sink | 38.159 |
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| refrigerator | 52.876 | book | 15.030 | clock | 48.622 |
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| vase | 37.013 | scissors | 26.307 | teddy bear | 45.676 |
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| hair drier | 7.255 | toothbrush | 19.374 | | |
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## License
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All files in this directory are licensed under [Apache 2.0 License](./LICENSE).
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#### Contributor Details
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- Google Summer of Code'22
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- Contributor: Sri Siddarth Chakaravarthy
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- Github Profile: https://github.com/Sidd1609
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- Organisation: OpenCV
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- Project: Lightweight object detection models using OpenCV
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## Reference
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- YOLOX article: https://arxiv.org/abs/2107.08430
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- YOLOX weight and scripts for training: https://github.com/Megvii-BaseDetection/YOLOX
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- YOLOX blog: https://arshren.medium.com/yolox-new-improved-yolo-d430c0e4cf20
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- YOLOX-lite: https://github.com/TexasInstruments/edgeai-yolox

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