YOLOv4-large

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

Model	Test Size	AP^test	AP₅₀^test	AP₇₅^test	AP_S^test	AP_M^test	AP_L^test	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	AP^val	AP₅₀^val	AP₇₅^val	AP_S^val	AP_M^val	AP_L^val	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	AP^val	AP₅₀^val	AP₇₅^val	AP_S^val	AP_M^val	AP_L^val
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

Implementation of "Scaled-YOLOv4: Scaling Cross Stage Partial Network" using PyTorch framwork.

Related tags

Overview

YOLOv4-large

Installation

Testing

Training

Citation

Acknowledgements

Owner

Kin-Yiu, Wong

A PyTorch Toolbox for Face Recognition

Madanalysis5 - A package for event file analysis and recasting of LHC results

Code for HLA-Face: Joint High-Low Adaptation for Low Light Face Detection (CVPR21)

mmfewshot is an open source few shot learning toolbox based on PyTorch

Turi Create simplifies the development of custom machine learning models.

Rethinking Transformer-based Set Prediction for Object Detection

LF-YOLO (Lighter and Faster YOLO) is used to detect defect of X-ray weld image.

Infrastructure as Code (IaC) for a self-hosted version of Gnosis Safe on AWS

Code release for DS-NeRF (Depth-supervised Neural Radiance Fields)

PyTorch implementation of Super SloMo by Jiang et al.

Keeping it safe - AI Based COVID-19 Tracker using Deep Learning and facial recognition

Keras implementation of PersonLab for Multi-Person Pose Estimation and Instance Segmentation.

Joint project of the duo Hacker Ninjas

2021 Artificial Intelligence Diabetes Datathon

An implementation for the ICCV 2021 paper Deep Permutation Equivariant Structure from Motion.

Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation, CVPR 2018

Learning to See by Looking at Noise

Fortuitous Forgetting in Connectionist Networks

Practical Single-Image Super-Resolution Using Look-Up Table

EdiBERT, a generative model for image editing