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https://github.com/lingtengqiu/Yolo_Nano
Pytorch implementation of yolo_Nano for pedestrian detection
https://github.com/lingtengqiu/Yolo_Nano
Last synced: 3 months ago
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Pytorch implementation of yolo_Nano for pedestrian detection
- Host: GitHub
- URL: https://github.com/lingtengqiu/Yolo_Nano
- Owner: lingtengqiu
- Created: 2019-12-26T10:34:01.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2021-04-27T13:45:48.000Z (almost 4 years ago)
- Last Synced: 2024-08-02T01:18:28.407Z (7 months ago)
- Language: Python
- Size: 14.8 MB
- Stars: 140
- Watchers: 7
- Forks: 15
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-yolo-object-detection - lingtengqiu/Yolo_Nano
- awesome-yolo-object-detection - lingtengqiu/Yolo_Nano
README
# PyTorch-YOLO_Nano
A minimal PyTorch implementation of YOLO_Nano
- [Yolo_Nano](https://arxiv.org/abs/1910.01271)
- [Bag of Freebies for Training Object Detection Neural Networks](https://arxiv.org/abs/1902.04103v3)
#### Trick here I have done
[Bag of Freebies for Training Object Detection Neural Networks](https://arxiv.org/abs/1902.04103v3) tell us that fixup in object detection can increase the mAP, So I realize it and test in result.
- [x] Data Augmentation
- [x] Fixup
- [x] Cosine lr decay
- [x] Warm up
- [ ] multi-GPU
#### Download COCO
$ cd data/
$ bash get_coco_dataset.sh
## Module Pipeline
## training
```bash
bash train.sh
Better Para:
--epochs 120
--batch_size 8
--model_def ./config/yolo-nano_person.cfg
--lr 2.5e-4
--fix_up True
--lr_policy cosine
```
## Testing
```bash
python test.py --data_config ./config/coco_person.data --model_def ./config/yolo-nano_person.cfg --weights_path [checkpoint path]
```
## Result
In this engineer we only train our model using coco-train person class
we compare with yolov-3，yolo-tiny. We got competitive results.
Methods |mAP@50|mAP|weights|FPS| Model
:--------------:|:--:|:--:|:--: |:--: |:--:
yolov3(paper) | 74.4 |40.3 | 204.8M| 28.6FPS |[Google Disk](https://pjreddie.com/media/files/yolov3.weights)
yolov3-tiny(paper) | 38.8 |15.6 | 35.4M | 45FPS |[Google Disk](https://pjreddie.com/media/files/yolov3-tiny.weights)
yolo-nano | 55.6 |27.7 | 22.0M | 40FPS |[Baidu WebDisk](https://pan.baidu.com/s/1Rp0is2LqA91XwjRc41mGaw)
Baidu WebDisk Key: p2j3
## Ablation Result
Augmentation| fixup | mAP
:--------------:|:--:|:--:
No|No|54.3
Yes|No|53.9
No|YES|55.6
YES|YES|54.8
## Inference Result
