/ScaledYOLOv4

Scaled-YOLOv4: Scaling Cross Stage Partial Network

Primary LanguagePythonGNU General Public License v3.0GPL-3.0

Setup for triton

Local working copy to verify Conda environment

  1. create conda env for python 3.8
  2. conda install cudatoolkit=10.2
  3. pip install -r requirements.txt
  4. git clone mish-cuda with git clone https://github.com/JunnYu/mish-cuda
  5. enter mish-cuda repo and run python setup.py build install
  6. test with webcam using python detect.py --weights weights/yolov4-p5.pt --source 0 or image python detect.py --weights weights/yolov4-p5.pt --source inference/images/dog.jpg --save-img

Configurations for triton

  1. pack conda environment with conda-pack
  2. build image with triton/Dockerfile
  3. edit triton/models/yolov4/config.pbtxt
parameters: {
  key: "EXECUTION_ENV_PATH",
  value: {string_value: "/yolov4_env.tar.gz"} <---- this should be the address you will mount it on the docker container
}
  1. run triton server by going to triton/ and running bash run_server.sh. make sure to change container names accordingly

Using the triton server

  1. activate the conda environment the last 2 packages inside requirements.txt are required to run the triton client(nvidia-pyindex, tritonclient[all])
  2. run the client script with python client.py

YOLOv4-large

This is the implementation of "Scaled-YOLOv4: Scaling Cross Stage Partial Network" using PyTorch framwork.

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
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
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
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
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
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

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