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Merge pull request #103 from Sidd1609:NanoDet
add object detection model NanoDet
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# Nanodet
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Nanodet: NanoDet is a FCOS-style one-stage anchor-free object detection model which using Generalized Focal Loss as classification and regression loss.In NanoDet-Plus, we propose a novel label assignment strategy with a simple assign guidance module (AGM) and a dynamic soft label assigner (DSLA) to solve the optimal label assignment problem in lightweight model training.
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Note:
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- This version of nanodet: Nanodet-m-plus-1.5x_416
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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,
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![test1_res.jpg](./samples/1_res.jpg)
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![test2_res.jpg](./samples/2_res.jpg)
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Video inference result,
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![WebCamR.gif](./samples/WebCamR.gif)
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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 | 0.304 |
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| all | 0.50 | 0.459 |
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| all | 0.75 | 0.317 |
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| small | 0.50:0.95 | 0.107 |
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| medium | 0.50:0.95 | 0.322 |
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| large | 0.50:0.95 | 0.478 |
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</td><td>
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area | IoU | Average Recall |
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|:-------|:------|:----------------|
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| all | 0.50:0.95 | 0.278 |
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| all | 0.50:0.95 | 0.434 |
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| all | 0.50:0.95 | 0.462 |
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| small | 0.50:0.95 | 0.198 |
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| medium | 0.50:0.95 | 0.510 |
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| large | 0.50:0.95 | 0.702 |
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</td></tr> </table>
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| class | AP50 | mAP | class | AP50 | mAP |
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|:--------------|:-------|:------|:---------------|:-------|:------|
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| person | 67.5 | 41.8 | bicycle | 35.4 | 18.8 |
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| car | 45.0 | 25.4 | motorcycle | 58.9 | 33.1 |
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| airplane | 77.3 | 58.9 | bus | 68.8 | 56.4 |
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| train | 81.1 | 60.5 | truck | 38.6 | 24.7 |
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| boat | 35.5 | 16.7 | traffic light | 30.5 | 14.0 |
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| fire hydrant | 69.8 | 54.5 | stop sign | 60.9 | 54.6 |
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| parking meter | 55.1 | 38.5 | bench | 26.8 | 15.9 |
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| bird | 38.3 | 23.6 | cat | 82.5 | 62.1 |
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| dog | 67.0 | 51.4 | horse | 64.3 | 44.2 |
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| sheep | 57.7 | 35.8 | cow | 61.2 | 39.9 |
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| elephant | 79.9 | 56.2 | bear | 81.8 | 63.0 |
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| zebra | 85.4 | 59.5 | giraffe | 84.1 | 59.9 |
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| backpack | 12.4 | 5.9 | umbrella | 46.5 | 28.8 |
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| handbag | 8.4 | 3.7 | tie | 35.2 | 19.6 |
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| suitcase | 38.1 | 23.8 | frisbee | 60.7 | 43.9 |
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| skis | 30.5 | 14.5 | snowboard | 32.3 | 18.2 |
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| sports ball | 37.6 | 24.5 | kite | 51.1 | 30.4 |
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| baseball bat | 28.9 | 13.6 | baseball glove | 40.1 | 21.6 |
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| skateboard | 59.4 | 35.2 | surfboard | 47.9 | 26.6 |
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| tennis racket | 55.2 | 30.5 | bottle | 34.7 | 20.2 |
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| wine glass | 27.8 | 16.3 | cup | 35.5 | 23.7 |
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| fork | 25.9 | 14.8 | knife | 10.9 | 5.6 |
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| spoon | 8.7 | 4.1 | bowl | 42.8 | 29.4 |
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| banana | 35.5 | 18.5 | apple | 19.4 | 12.9 |
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| sandwich | 46.7 | 33.4 | orange | 35.2 | 25.9 |
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| broccoli | 36.4 | 19.1 | carrot | 30.9 | 17.8 |
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| hot dog | 42.7 | 29.3 | pizza | 61.0 | 44.9 |
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| donut | 47.3 | 34.0 | cake | 39.9 | 24.4 |
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| chair | 28.8 | 16.1 | couch | 60.5 | 42.6 |
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| potted plant | 29.0 | 15.3 | bed | 63.3 | 46.0 |
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| dining table | 39.6 | 27.5 | toilet | 71.3 | 55.3 |
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| tv | 66.5 | 48.1 | laptop | 62.6 | 46.9 |
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| mouse | 63.5 | 44.1 | remote | 19.8 | 10.3 |
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| keyboard | 62.1 | 41.5 | cell phone | 33.7 | 22.8 |
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| microwave | 54.9 | 39.6 | oven | 48.1 | 30.4 |
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| toaster | 30.0 | 16.4 | sink | 44.5 | 27.8 |
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| refrigerator | 63.2 | 46.1 | book | 18.4 | 7.3 |
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| clock | 57.8 | 35.8 | vase | 33.7 | 22.1 |
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| scissors | 27.8 | 17.8 | teddy bear | 54.1 | 35.4 |
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| hair drier | 2.9 | 1.1 | toothbrush | 13.1 | 8.2 |
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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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- Nanodet: https://zhuanlan.zhihu.com/p/306530300
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- Nanodet Plus: https://zhuanlan.zhihu.com/p/449912627
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- Nanodet weight and scripts for training: https://github.com/RangiLyu/nanodet

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