Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/wongkinyiu/scaledyolov4
Scaled-YOLOv4: Scaling Cross Stage Partial Network
https://github.com/wongkinyiu/scaledyolov4
deep-learning machine-learning ml object-detection pytorch scaled-yolov4 yolo yolov3 yolov4 yolov4-csp yolov4-large yolov4-tiny
Last synced: 25 days ago
JSON representation
Scaled-YOLOv4: Scaling Cross Stage Partial Network
- Host: GitHub
- URL: https://github.com/wongkinyiu/scaledyolov4
- Owner: WongKinYiu
- License: gpl-3.0
- Created: 2020-11-14T03:01:11.000Z (almost 4 years ago)
- Default Branch: yolov4-large
- Last Pushed: 2023-07-19T11:06:55.000Z (over 1 year ago)
- Last Synced: 2024-10-15T04:03:43.857Z (25 days ago)
- Topics: deep-learning, machine-learning, ml, object-detection, pytorch, scaled-yolov4, yolo, yolov3, yolov4, yolov4-csp, yolov4-large, yolov4-tiny
- Language: Python
- Homepage:
- Size: 480 KB
- Stars: 2,020
- Watchers: 42
- Forks: 575
- Open Issues: 311
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# YOLOv4-large
This is the implementation of "[Scaled-YOLOv4: Scaling Cross Stage Partial Network](https://arxiv.org/abs/2011.08036)" using PyTorch framwork.
* [YOLOv4-CSP](https://github.com/WongKinYiu/ScaledYOLOv4/tree/yolov4-csp)
* [YOLOv4-tiny](https://github.com/WongKinYiu/ScaledYOLOv4/tree/yolov4-tiny)
* [YOLOv4-large](https://github.com/WongKinYiu/ScaledYOLOv4/tree/yolov4-large)
| Model | Test Size | APtest | AP50test | AP75test | APStest | APMtest | APLtest | batch1 throughput |
| :-- | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: |
| **YOLOv4-P5** | 896 | **51.4%** | **69.9%** | **56.3%** | **33.1%** | **55.4%** | **62.4%** | 41 *fps* |
| **YOLOv4-P5** | TTA | **52.5%** | **70.3%** | **58.0%** | **36.0%** | **52.4%** | **62.3%** | - |
| | | | | | | |
| **YOLOv4-P6** | 1280 | **54.3%** | **72.3%** | **59.5%** | **36.6%** | **58.2%** | **65.5%** | 30 *fps* |
| **YOLOv4-P6** | TTA | **54.9%** | **72.6%** | **60.2%** | **37.4%** | **58.8%** | **66.7%** | - |
| | | | | | | |
| **YOLOv4-P7** | 1536 | **55.4%** | **73.3%** | **60.7%** | **38.1%** | **59.5%** | **67.4%** | 15 *fps* |
| **YOLOv4-P7** | TTA | **55.8%** | **73.2%** | **61.2%** | **38.8%** | **60.1%** | **68.2%** | - |
| | | | | | | |
| Model | Test Size | APval | AP50val | AP75val | APSval | APMval | APLval | weights |
| :-- | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: |
| **YOLOv4-P5** | 896 | **51.2%** | **69.8%** | **56.2%** | **35.0%** | **56.2%** | **64.0%** | [`yolov4-p5.pt`](https://drive.google.com/file/d/1aXZZE999sHMP1gev60XhNChtHPRMH3Fz/view?usp=sharing) |
| **YOLOv4-P5** | TTA | **52.5%** | **70.2%** | **57.8%** | **38.5%** | **57.2%** | **64.0%** | - |
| **YOLOv4-P5** (+BoF) | 896 | **51.7%** | **70.3%** | **56.7%** | **35.9%** | **56.7%** | **64.3%** | [`yolov4-p5_.pt`](https://drive.google.com/file/d/15CL05ZufFk2krbRS993fqlG40Wb0HTyr/view?usp=sharing) |
| **YOLOv4-P5** (+BoF) | TTA | **52.8%** | **70.6%** | **58.3%** | **38.8%** | **57.4%** | **64.4%** | - |
| | | | | | | | |
| **YOLOv4-P6** | 1280 | **53.9%** | **72.0%** | **59.0%** | **39.3%** | **58.3%** | **66.6%** | [`yolov4-p6.pt`](https://drive.google.com/file/d/1aB7May8oPYzBqbgwYSZHuATPXyxh9xnf/view?usp=sharing) |
| **YOLOv4-P6** | TTA | **54.4%** | **72.3%** | **59.6%** | **39.8%** | **58.9%** | **67.6%** | - |
| **YOLOv4-P6** (+BoF) | 1280 | **54.4%** | **72.7%** | **59.5%** | **39.5%** | **58.9%** | **67.3%** | [`yolov4-p6_.pt`](https://drive.google.com/file/d/1Q8oG3lBVVoS0-UwNOBsDsPkq9VKs9UcC/view?usp=sharing) |
| **YOLOv4-P6** (+BoF) | TTA | **54.8%** | **72.6%** | **60.0%** | **40.6%** | **59.1%** | **68.2%** | - |
| **YOLOv4-P6** (+BoF*) | 1280 | **54.7%** | **72.9%** | **60.0%** | **39.4%** | **59.2%** | **68.3%** | |
| **YOLOv4-P6** (+BoF*) | TTA | **55.3%** | **73.2%** | **60.8%** | **40.5%** | **59.9%** | **69.4%** | - |
| | | | | | | | |
| **YOLOv4-P7** | 1536 | **55.0%** | **72.9%** | **60.2%** | **39.8%** | **59.9%** | **68.4%** | [`yolov4-p7.pt`](https://drive.google.com/file/d/18fGlzgEJTkUEiBG4hW00pyedJKNnYLP3/view?usp=sharing) |
| **YOLOv4-P7** | TTA | **55.5%** | **72.9%** | **60.8%** | **41.1%** | **60.3%** | **68.9%** | - |
| | | | | | | | |
| Model | Test Size | APval | AP50val | AP75val | APSval | APMval | APLval |
| :-- | :-: | :-: | :-: | :-: | :-: | :-: | :-: |
| **YOLOv4-P6-attention** | 1280 | **54.3%** | **72.3%** | **59.6%** | **38.7%** | **58.9%** | **66.6%** |
## Installation
```
# create the docker container, you can change the share memory size if you have more.
nvidia-docker run --name yolov4_csp -it -v your_coco_path/:/coco/ -v your_code_path/:/yolo --shm-size=64g nvcr.io/nvidia/pytorch:20.06-py3
# install mish-cuda, if you use different pytorch version, you could try https://github.com/thomasbrandon/mish-cuda
cd /
git clone https://github.com/JunnYu/mish-cuda
cd mish-cuda
python setup.py build install
# go to code folder
cd /yolo
```
## Testing
```
# download {yolov4-p5.pt, yolov4-p6.pt, yolov4-p7.pt} and put them in /yolo/weights/ folder.
python test.py --img 896 --conf 0.001 --batch 8 --device 0 --data coco.yaml --weights weights/yolov4-p5.pt
python test.py --img 1280 --conf 0.001 --batch 8 --device 0 --data coco.yaml --weights weights/yolov4-p6.pt
python test.py --img 1536 --conf 0.001 --batch 8 --device 0 --data coco.yaml --weights weights/yolov4-p7.pt
```
You will get following results:
```
# yolov4-p5
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.51244
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.69771
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.56180
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.35021
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.56247
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.63983
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.38530
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.64048
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.69801
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.55487
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.74368
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.82826
```
```
# yolov4-p6
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.53857
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.72015
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.59025
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.39285
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.58283
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.66580
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.39552
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.66504
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.72141
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.59193
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.75844
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.83981
```
```
# yolov4-p7
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.55046
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.72925
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.60224
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.39836
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.59854
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.68405
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.40256
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.66929
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.72943
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.59943
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.76873
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.84460
```
## Training
We use multiple GPUs for training.
{YOLOv4-P5, YOLOv4-P6, YOLOv4-P7} use input resolution {896, 1280, 1536} for training respectively.
```
# yolov4-p5
python -m torch.distributed.launch --nproc_per_node 4 train.py --batch-size 64 --img 896 896 --data coco.yaml --cfg yolov4-p5.yaml --weights '' --sync-bn --device 0,1,2,3 --name yolov4-p5
python -m torch.distributed.launch --nproc_per_node 4 train.py --batch-size 64 --img 896 896 --data coco.yaml --cfg yolov4-p5.yaml --weights 'runs/exp0_yolov4-p5/weights/last_298.pt' --sync-bn --device 0,1,2,3 --name yolov4-p5-tune --hyp 'data/hyp.finetune.yaml' --epochs 450 --resume
```
If your training process stucks, it due to bugs of the python.
Just `Ctrl+C` to stop training and resume training by:
```
# yolov4-p5
python -m torch.distributed.launch --nproc_per_node 4 train.py --batch-size 64 --img 896 896 --data coco.yaml --cfg yolov4-p5.yaml --weights 'runs/exp0_yolov4-p5/weights/last.pt' --sync-bn --device 0,1,2,3 --name yolov4-p5 --resume
```
## Citation
```
@InProceedings{Wang_2021_CVPR,
author = {Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
title = {{Scaled-YOLOv4}: Scaling Cross Stage Partial Network},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {13029-13038}
}
```
## Acknowledgements
Expand
* [https://github.com/AlexeyAB/darknet](https://github.com/AlexeyAB/darknet)
* [https://github.com/WongKinYiu/PyTorch_YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4)
* [https://github.com/ultralytics/yolov3](https://github.com/ultralytics/yolov3)
* [https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5)