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# Nanodet
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.
Note:
- This version of nanodet: Nanodet-m-plus-1.5x_416
## Demo
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
```
Note:
- image result saved as "result.jpg"
## Results
Here are some of the sample results that were observed using the model,
Check [benchmark/download_data.py](../../benchmark/download_data.py) for the original images.
Video inference result,
## 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 | 0.304 |
| all | 0.50 | 0.459 |
| all | 0.75 | 0.317 |
| small | 0.50:0.95 | 0.107 |
| medium | 0.50:0.95 | 0.322 |
| large | 0.50:0.95 | 0.478 |
</td><td>
area | IoU | Average Recall |
|:-------|:------|:----------------|
| all | 0.50:0.95 | 0.278 |
| all | 0.50:0.95 | 0.434 |
| all | 0.50:0.95 | 0.462 |
| small | 0.50:0.95 | 0.198 |
| medium | 0.50:0.95 | 0.510 |
| large | 0.50:0.95 | 0.702 |
</td></tr> </table>
| class | AP50 | mAP | class | AP50 | mAP |
|:--------------|:-------|:------|:---------------|:-------|:------|
| person | 67.5 | 41.8 | bicycle | 35.4 | 18.8 |
| car | 45.0 | 25.4 | motorcycle | 58.9 | 33.1 |
| airplane | 77.3 | 58.9 | bus | 68.8 | 56.4 |
| train | 81.1 | 60.5 | truck | 38.6 | 24.7 |
| boat | 35.5 | 16.7 | traffic light | 30.5 | 14.0 |
| fire hydrant | 69.8 | 54.5 | stop sign | 60.9 | 54.6 |
| parking meter | 55.1 | 38.5 | bench | 26.8 | 15.9 |
| bird | 38.3 | 23.6 | cat | 82.5 | 62.1 |
| dog | 67.0 | 51.4 | horse | 64.3 | 44.2 |
| sheep | 57.7 | 35.8 | cow | 61.2 | 39.9 |
| elephant | 79.9 | 56.2 | bear | 81.8 | 63.0 |
| zebra | 85.4 | 59.5 | giraffe | 84.1 | 59.9 |
| backpack | 12.4 | 5.9 | umbrella | 46.5 | 28.8 |
| handbag | 8.4 | 3.7 | tie | 35.2 | 19.6 |
| suitcase | 38.1 | 23.8 | frisbee | 60.7 | 43.9 |
| skis | 30.5 | 14.5 | snowboard | 32.3 | 18.2 |
| sports ball | 37.6 | 24.5 | kite | 51.1 | 30.4 |
| baseball bat | 28.9 | 13.6 | baseball glove | 40.1 | 21.6 |
| skateboard | 59.4 | 35.2 | surfboard | 47.9 | 26.6 |
| tennis racket | 55.2 | 30.5 | bottle | 34.7 | 20.2 |
| wine glass | 27.8 | 16.3 | cup | 35.5 | 23.7 |
| fork | 25.9 | 14.8 | knife | 10.9 | 5.6 |
| spoon | 8.7 | 4.1 | bowl | 42.8 | 29.4 |
| banana | 35.5 | 18.5 | apple | 19.4 | 12.9 |
| sandwich | 46.7 | 33.4 | orange | 35.2 | 25.9 |
| broccoli | 36.4 | 19.1 | carrot | 30.9 | 17.8 |
| hot dog | 42.7 | 29.3 | pizza | 61.0 | 44.9 |
| donut | 47.3 | 34.0 | cake | 39.9 | 24.4 |
| chair | 28.8 | 16.1 | couch | 60.5 | 42.6 |
| potted plant | 29.0 | 15.3 | bed | 63.3 | 46.0 |
| dining table | 39.6 | 27.5 | toilet | 71.3 | 55.3 |
| tv | 66.5 | 48.1 | laptop | 62.6 | 46.9 |
| mouse | 63.5 | 44.1 | remote | 19.8 | 10.3 |
| keyboard | 62.1 | 41.5 | cell phone | 33.7 | 22.8 |
| microwave | 54.9 | 39.6 | oven | 48.1 | 30.4 |
| toaster | 30.0 | 16.4 | sink | 44.5 | 27.8 |
| refrigerator | 63.2 | 46.1 | book | 18.4 | 7.3 |
| clock | 57.8 | 35.8 | vase | 33.7 | 22.1 |
| scissors | 27.8 | 17.8 | teddy bear | 54.1 | 35.4 |
| hair drier | 2.9 | 1.1 | toothbrush | 13.1 | 8.2 |
## 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
- Nanodet: https://zhuanlan.zhihu.com/p/306530300
- Nanodet Plus: https://zhuanlan.zhihu.com/p/449912627
- Nanodet weight and scripts for training: https://github.com/RangiLyu/nanodet
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