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

Cognate Detection Repository

Fewshot-face-translation-GAN - Generative adversarial networks integrating modules from FUNIT and SPADE for face-swapping.

Machine learning framework for both deep learning and traditional algorithms

Implementation of "Semi-supervised Domain Adaptive Structure Learning"

Code for the paper "Learning-Augmented Algorithms for Online Steiner Tree"

An AutoML Library made with Optuna and PyTorch Lightning

An improvement of FasterGICP: Acceptance-rejection Sampling based 3D Lidar Odometry

Basics of 2D and 3D Human Pose Estimation.

A large-scale database for graph representation learning

Process text, including tokenizing and representing sentences as vectors and Applying some concepts like RNN, LSTM and GRU to create a classifier can detect the language in which a sentence is written from among 17 languages.

Hyper-parameter optimization for sklearn

3DV 2021: Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry

PyTorch implementation of DirectCLR from paper Understanding Dimensional Collapse in Contrastive Self-supervised Learning

AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation

Deep Convolutional Generative Adversarial Networks

Automatic deep learning for image classification.

PyTorch code for EMNLP 2021 paper: Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System

Accepted at ICCV-2021: Workshop on Computer Vision for Automated Medical Diagnosis (CVAMD)

Bringing sanity to world of messed-up data

PyTorch implementation of Self-supervised Contrastive Regularization for DG (SelfReg)