YOLOv4

This is PyTorch implementation of YOLOv4 which is based on ultralytics/yolov3.

development log

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2021-10-31 - support RS loss, aLRP loss, AP loss.
2021-10-30 - support alpha IoU.
2021-10-20 - design resolution calibration methods.
2021-10-15 - support joint detection, instance segmentation, and semantic segmentation. seg-yolo
2021-10-13 - design ratio yolo.
2021-09-22 - pytorch 1.9 compatibility.
2021-09-21 - support DIM.
2021-09-16 - support Dynamic Head.
2021-08-28 - design domain adaptive training.
2021-08-22 - design re-balance models.
2021-08-21 - support simOTA.
2021-08-14 - design approximation-based methods.
2021-07-27 - design new decoders.
2021-07-22 - support 1) decoupled head, 2) anchor-free, and 3) multi positives in yolox.
2021-07-10 - design distribution-based implicit modeling.
2021-07-06 - support outlooker attention. volo
2021-07-06 - design self emsemble training method.
2021-06-23 - design cross multi-stage correlation module.
2021-06-18 - design cross stage cross correlation module.
2021-06-17 - support cross correlation module. ccn
2021-06-17 - support attention modules. cbam saan
2021-04-20 - support swin transformer. swin
2021-03-16 - design new stem layers.
2021-03-13 - design implicit modeling. nn mf lc
2021-01-26 - support vision transformer. tr
2021-01-26 - design mask objectness.
2021-01-25 - design rotate augmentation.
2021-01-23 - design collage augmentation.
2021-01-22 - support VoVNet, VoVNetv2.
2021-01-22 - support EIoU.
2021-01-19 - support instance segmentation. mask-yolo
2021-01-17 - support anchor-free-based methods. center-yolo
2021-01-14 - support joint detection and classification. classify-yolo
2020-01-02 - design new PRN and CSP-based models.
2020-12-22 - support transfer learning.
2020-12-18 - support non-local series self-attention blocks. gc dnl
2020-12-16 - support down-sampling blocks in cspnet paper. down-c down-d
2020-12-03 - support imitation learning.
2020-12-02 - support squeeze and excitation.
2020-11-26 - support multi-class multi-anchor joint detection and embedding.
2020-11-25 - support joint detection and embedding. track-yolo
2020-11-23 - support teacher-student learning.
2020-11-17 - pytorch 1.7 compatibility.
2020-11-06 - support inference with initial weights.
2020-10-21 - fully supported by darknet.
2020-09-18 - design fine-tune methods.
2020-08-29 - support deformable kernel.
2020-08-25 - pytorch 1.6 compatibility.
2020-08-24 - support channel last training/testing.
2020-08-16 - design CSPPRN.
2020-08-15 - design deeper model. csp-p6-mish
2020-08-11 - support HarDNet. hard39-pacsp hard68-pacsp hard85-pacsp
2020-08-10 - add DDP training.
2020-08-06 - support DCN, DCNv2. yolov4-dcn
2020-08-01 - add pytorch hub.
2020-07-31 - support ResNet, ResNeXt, CSPResNet, CSPResNeXt. r50-pacsp x50-pacsp cspr50-pacsp cspx50-pacsp
2020-07-28 - support SAM. yolov4-pacsp-sam
2020-07-24 - update api.
2020-07-23 - support CUDA accelerated Mish activation function.
2020-07-19 - support and training tiny YOLOv4. yolov4-tiny
2020-07-15 - design and training conditional YOLOv4. yolov4-pacsp-conditional
2020-07-13 - support MixUp data augmentation.
2020-07-03 - design new stem layers.
2020-06-16 - support floating16 of GPU inference.
2020-06-14 - convert .pt to .weights for darknet fine-tuning.
2020-06-13 - update multi-scale training strategy.
2020-06-12 - design scaled YOLOv4 follow ultralytics. yolov4-pacsp-s yolov4-pacsp-m yolov4-pacsp-l yolov4-pacsp-x
2020-06-07 - design scaling methods for CSP-based models. yolov4-pacsp-25 yolov4-pacsp-75
2020-06-03 - update COCO2014 to COCO2017.
2020-05-30 - update FPN neck to CSPFPN. yolov4-yocsp yolov4-yocsp-mish
2020-05-24 - update neck of YOLOv4 to CSPPAN. yolov4-pacsp yolov4-pacsp-mish
2020-05-15 - training YOLOv4 with Mish activation function. yolov4-yospp-mish yolov4-paspp-mish
2020-05-08 - design and training YOLOv4 with FPN neck. yolov4-yospp
2020-05-01 - training YOLOv4 with Leaky activation function using PyTorch. yolov4-paspp PAN

Pretrained Models & Comparison

Model	Test Size	AP^test	AP₅₀^test	AP₇₅^test	AP_S^test	AP_M^test	AP_L^test	cfg	weights
YOLOv4	640	50.0%	68.4%	54.7%	30.5%	54.3%	63.3%	cfg	weights

YOLOv4_pacsp-s	640	39.0%	57.8%	42.4%	20.6%	42.6%	50.0%	cfg	weights
YOLOv4_pacsp	640	49.8%	68.4%	54.3%	30.1%	54.0%	63.4%	cfg	weights
YOLOv4_pacsp-x	640	52.2%	70.5%	56.8%	32.7%	56.3%	65.9%	cfg	weights

YOLOv4_pacsp-s-mish	640	40.8%	59.5%	44.3%	22.4%	44.6%	51.8%	cfg	weights
YOLOv4_pacsp-mish	640	50.9%	69.4%	55.5%	31.2%	55.0%	64.7%	cfg	weights
YOLOv4_pacsp-x-mish	640	52.8%	71.1%	57.5%	33.6%	56.9%	66.6%	cfg	weights

Model	Test Size	AP^val	AP₅₀^val	AP₇₅^val	AP_S^val	AP_M^val	AP_L^val	cfg	weights
YOLOv4	640	49.7%	68.2%	54.3%	32.9%	54.8%	63.7%	cfg	weights

YOLOv4_pacsp-s	640	38.9%	57.7%	42.2%	21.9%	43.3%	51.9%	cfg	weights
YOLOv4_pacsp	640	49.4%	68.1%	53.8%	32.7%	54.2%	64.0%	cfg	weights
YOLOv4_pacsp-x	640	51.6%	70.1%	56.2%	35.3%	56.4%	66.9%	cfg	weights

YOLOv4_pacsp-s-mish	640	40.7%	59.5%	44.2%	25.3%	45.1%	53.4%	cfg	weights
YOLOv4_pacsp-mish	640	50.8%	69.4%	55.4%	34.3%	55.5%	65.7%	cfg	weights
YOLOv4_pacsp-x-mish	640	52.6%	71.0%	57.2%	36.4%	57.3%	67.6%	cfg	weights

archive

Model	Test Size	AP^val	AP₅₀^val	AP₇₅^val	AP_S^val	AP_M^val	AP_L^val	cfg	weights
YOLOv4	640	48.4%	67.1%	52.9%	31.7%	53.8%	62.0%	cfg	weights

YOLOv4_pacsp-s	640	37.0%	55.7%	40.0%	20.2%	41.6%	48.4%	cfg	weights
YOLOv4_pacsp	640	47.7%	66.4%	52.0%	32.3%	53.0%	61.7%	cfg	weights
YOLOv4_pacsp-x	640	50.0%	68.3%	54.5%	33.9%	55.4%	63.7%	cfg	weights

YOLOv4_pacsp-s-mish	640	38.8%	57.8%	42.0%	21.6%	43.7%	51.1%	cfg	weights
YOLOv4_pacsp-mish	640	48.8%	67.2%	53.4%	31.5%	54.4%	62.2%	cfg	weights
YOLOv4_pacsp-x-mish	640	51.2%	69.4%	55.9%	35.0%	56.5%	65.0%	cfg	weights

Model	Test Size	AP^val	AP₅₀^val	AP₇₅^val	AP_S^val	AP_M^val	AP_L^val	cfg	weights
YOLOv4	672	47.7%	66.7%	52.1%	30.5%	52.6%	61.4%	cfg	weights

YOLOv4_pacsp-s	672	36.6%	55.5%	39.6%	21.2%	41.1%	47.0%	cfg	weights
YOLOv4_pacsp	672	47.2%	66.2%	51.6%	30.4%	52.3%	60.8%	cfg	weights
YOLOv4_pacsp-x	672	49.3%	68.1%	53.6%	31.8%	54.5%	63.6%	cfg	weights

YOLOv4_pacsp-s-mish	672	38.6%	57.7%	41.8%	22.3%	43.5%	49.3%	cfg	weights
(+BoF)	640	39.9%	59.1%	43.1%	24.4%	45.2%	51.4%		weights
YOLOv4_pacsp-mish	672	48.1%	66.9%	52.3%	30.8%	53.4%	61.7%	cfg	weights
(+BoF)	640	49.3%	68.2%	53.8%	31.9%	54.9%	62.8%		weights
YOLOv4_pacsp-x-mish	672	50.0%	68.5%	54.4%	32.9%	54.9%	64.0%	cfg	weights
(+BoF)	640	51.0%	69.7%	55.5%	33.3%	56.2%	65.5%		weights

Requirements

docker (recommanded):

# create the docker container, you can change the share memory size if you have more.
nvidia-docker run --name yolov4 -it -v your_coco_path/:/coco/ -v your_code_path/:/yolo --shm-size=64g nvcr.io/nvidia/pytorch:20.11-py3

# apt install required packages
apt update
apt install -y zip htop screen libgl1-mesa-glx

# pip install required packages
pip install seaborn thop

# install mish-cuda if you want to use mish activation
# https://github.com/thomasbrandon/mish-cuda
# https://github.com/JunnYu/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

local:

pip install -r requirements.txt

※ For running Mish models, please install https://github.com/thomasbrandon/mish-cuda

Training

python train.py --device 0 --batch-size 16 --img 640 640 --data coco.yaml --cfg cfg/yolov4-pacsp.cfg --weights '' --name yolov4-pacsp

Testing

python test.py --img 640 --conf 0.001 --batch 8 --device 0 --data coco.yaml --cfg cfg/yolov4-pacsp.cfg --weights weights/yolov4-pacsp.pt

Citation

@article{bochkovskiy2020yolov4,
  title={{YOLOv4}: Optimal Speed and Accuracy of Object Detection},
  author={Bochkovskiy, Alexey and Wang, Chien-Yao and Liao, Hong-Yuan Mark},
  journal={arXiv preprint arXiv:2004.10934},
  year={2020}
}

@inproceedings{wang2020cspnet,
  title={{CSPNet}: A New Backbone That Can Enhance Learning Capability of {CNN}},
  author={Wang, Chien-Yao and Mark Liao, Hong-Yuan and Wu, Yueh-Hua and Chen, Ping-Yang and Hsieh, Jun-Wei and Yeh, I-Hau},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  pages={390--391},
  year={2020}
}

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Name		Name	Last commit message	Last commit date
Latest commit History 102 Commits
cfg		cfg
data		data
images		images
models		models
utils		utils
weights		weights
README.md		README.md
detect.py		detect.py
requirements.txt		requirements.txt
test.py		test.py
train.py		train.py

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