YOLOv4
This is PyTorch implementation of YOLOv4 which is based on ultralytics/yolov3.
development log
Expand
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 | APtest | AP50test | AP75test | APStest | APMtest | APLtest | cfg | weights |
---|---|---|---|---|---|---|---|---|---|
YOLOv4 | 640 | 50.0% | 68.4% | 54.7% | 30.5% | 54.3% | 63.3% | cfg | weights |
YOLOv4pacsp-s | 640 | 39.0% | 57.8% | 42.4% | 20.6% | 42.6% | 50.0% | cfg | weights |
YOLOv4pacsp | 640 | 49.8% | 68.4% | 54.3% | 30.1% | 54.0% | 63.4% | cfg | weights |
YOLOv4pacsp-x | 640 | 52.2% | 70.5% | 56.8% | 32.7% | 56.3% | 65.9% | cfg | weights |
YOLOv4pacsp-s-mish | 640 | 40.8% | 59.5% | 44.3% | 22.4% | 44.6% | 51.8% | cfg | weights |
YOLOv4pacsp-mish | 640 | 50.9% | 69.4% | 55.5% | 31.2% | 55.0% | 64.7% | cfg | weights |
YOLOv4pacsp-x-mish | 640 | 52.8% | 71.1% | 57.5% | 33.6% | 56.9% | 66.6% | cfg | weights |
Model | Test Size | APval | AP50val | AP75val | APSval | APMval | APLval | cfg | weights |
---|---|---|---|---|---|---|---|---|---|
YOLOv4 | 640 | 49.7% | 68.2% | 54.3% | 32.9% | 54.8% | 63.7% | cfg | weights |
YOLOv4pacsp-s | 640 | 38.9% | 57.7% | 42.2% | 21.9% | 43.3% | 51.9% | cfg | weights |
YOLOv4pacsp | 640 | 49.4% | 68.1% | 53.8% | 32.7% | 54.2% | 64.0% | cfg | weights |
YOLOv4pacsp-x | 640 | 51.6% | 70.1% | 56.2% | 35.3% | 56.4% | 66.9% | cfg | weights |
YOLOv4pacsp-s-mish | 640 | 40.7% | 59.5% | 44.2% | 25.3% | 45.1% | 53.4% | cfg | weights |
YOLOv4pacsp-mish | 640 | 50.8% | 69.4% | 55.4% | 34.3% | 55.5% | 65.7% | cfg | weights |
YOLOv4pacsp-x-mish | 640 | 52.6% | 71.0% | 57.2% | 36.4% | 57.3% | 67.6% | cfg | weights |
archive
Model | Test Size | APval | AP50val | AP75val | APSval | APMval | APLval | cfg | weights |
---|---|---|---|---|---|---|---|---|---|
YOLOv4 | 640 | 48.4% | 67.1% | 52.9% | 31.7% | 53.8% | 62.0% | cfg | weights |
YOLOv4pacsp-s | 640 | 37.0% | 55.7% | 40.0% | 20.2% | 41.6% | 48.4% | cfg | weights |
YOLOv4pacsp | 640 | 47.7% | 66.4% | 52.0% | 32.3% | 53.0% | 61.7% | cfg | weights |
YOLOv4pacsp-x | 640 | 50.0% | 68.3% | 54.5% | 33.9% | 55.4% | 63.7% | cfg | weights |
YOLOv4pacsp-s-mish | 640 | 38.8% | 57.8% | 42.0% | 21.6% | 43.7% | 51.1% | cfg | weights |
YOLOv4pacsp-mish | 640 | 48.8% | 67.2% | 53.4% | 31.5% | 54.4% | 62.2% | cfg | weights |
YOLOv4pacsp-x-mish | 640 | 51.2% | 69.4% | 55.9% | 35.0% | 56.5% | 65.0% | cfg | weights |
Model | Test Size | APval | AP50val | AP75val | APSval | APMval | APLval | cfg | weights |
---|---|---|---|---|---|---|---|---|---|
YOLOv4 | 672 | 47.7% | 66.7% | 52.1% | 30.5% | 52.6% | 61.4% | cfg | weights |
YOLOv4pacsp-s | 672 | 36.6% | 55.5% | 39.6% | 21.2% | 41.1% | 47.0% | cfg | weights |
YOLOv4pacsp | 672 | 47.2% | 66.2% | 51.6% | 30.4% | 52.3% | 60.8% | cfg | weights |
YOLOv4pacsp-x | 672 | 49.3% | 68.1% | 53.6% | 31.8% | 54.5% | 63.6% | cfg | weights |
YOLOv4pacsp-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 | |
YOLOv4pacsp-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 | |
YOLOv4pacsp-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
β» 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}
}