YOLOv4
This is PyTorch implementation of YOLOv4 which is based on ultralytics/yolov3.
development log
Expand
* `2021-10-31` - support [RS loss](https://arxiv.org/abs/2107.11669), [aLRP loss](https://arxiv.org/abs/2009.13592), [AP loss](https://arxiv.org/abs/2008.07294).
* `2021-10-30` - support [alpha IoU](https://arxiv.org/abs/2110.13675).
* `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](https://arxiv.org/abs/1808.06670).
* `2021-09-16` - support [Dynamic Head](https://arxiv.org/abs/2106.08322).
* `2021-08-28` - design domain adaptive training.
* `2021-08-22` - design re-balance models.
* `2021-08-21` - support [simOTA](https://arxiv.org/abs/2107.08430).
* `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](https://arxiv.org/abs/2107.08430).
* `2021-07-10` - design distribution-based implicit modeling.
* `2021-07-06` - support outlooker attention. [`volo`](https://arxiv.org/abs/2106.13112)
* `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`](https://arxiv.org/abs/2010.12138)
* `2021-06-17` - support attention modules. [`cbam`](https://arxiv.org/abs/1807.06521) [`saan`](https://arxiv.org/abs/2010.12138)
* `2021-04-20` - support swin transformer. [`swin`](https://arxiv.org/abs/2103.14030)
* `2021-03-16` - design new stem layers.
* `2021-03-13` - design implicit modeling. [`nn`]() [`mf`]() [`lc`]()
* `2021-01-26` - support vision transformer. [`tr`](https://arxiv.org/abs/2010.11929)
* `2021-01-26` - design mask objectness.
* `2021-01-25` - design rotate augmentation.
* `2021-01-23` - design collage augmentation.
* `2021-01-22` - support [VoVNet](https://arxiv.org/abs/1904.09730), [VoVNetv2](https://arxiv.org/abs/1911.06667).
* `2021-01-22` - support [EIoU](https://arxiv.org/abs/2101.08158).
* `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](https://github.com/WongKinYiu/PartialResidualNetworks) and [CSP](https://github.com/WongKinYiu/CrossStagePartialNetworks)-based models.
* `2020-12-22` - support transfer learning.
* `2020-12-18` - support non-local series self-attention blocks. [`gc`](https://arxiv.org/abs/1904.11492) [`dnl`](https://arxiv.org/abs/2006.06668)
* `2020-12-16` - support down-sampling blocks in cspnet paper. [`down-c`]() [`down-d`](https://arxiv.org/abs/1812.01187)
* `2020-12-03` - support imitation learning.
* `2020-12-02` - support [squeeze and excitation](https://arxiv.org/abs/1709.01507).
* `2020-11-26` - support multi-class multi-anchor joint detection and embedding.
* `2020-11-25` - support [joint detection and embedding](https://arxiv.org/abs/1909.12605). [`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](https://arxiv.org/abs/1910.02940).
* `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](https://arxiv.org/abs/1909.00948). [`hard39-pacsp`]() [`hard68-pacsp`]() [`hard85-pacsp`]()
* `2020-08-10` - add DDP training.
* `2020-08-06` - support [DCN](https://arxiv.org/abs/1703.06211), [DCNv2](https://arxiv.org/abs/1811.11168). [`yolov4-dcn`]()
* `2020-08-01` - add pytorch hub.
* `2020-07-31` - support [ResNet](https://arxiv.org/abs/1512.03385), [ResNeXt](https://arxiv.org/abs/1611.05431), [CSPResNet](https://github.com/WongKinYiu/CrossStagePartialNetworks), [CSPResNeXt](https://github.com/WongKinYiu/CrossStagePartialNetworks). [`r50-pacsp`]() [`x50-pacsp`]() [`cspr50-pacsp`]() [`cspx50-pacsp`]()
* `2020-07-28` - support [SAM](https://arxiv.org/abs/2004.10934). [`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](https://arxiv.org/abs/1710.09412) 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](https://github.com/ultralytics/yolov5). [`yolov4-pacsp-s`]() [`yolov4-pacsp-m`]() [`yolov4-pacsp-l`]() [`yolov4-pacsp-x`]()
* `2020-06-07` - design [scaling methods](https://github.com/WongKinYiu/PyTorch_YOLOv4/blob/master/images/scalingCSP.png) 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](https://arxiv.org/abs/1612.03144) neck. [`yolov4-yospp`]()
* `2020-05-01` - training YOLOv4 with Leaky activation function using PyTorch. [`yolov4-paspp`]() [`PAN`](https://arxiv.org/abs/1803.01534)
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](https://github.com/WongKinYiu/PyTorch_YOLOv4/blob/master/cfg/yolov4.cfg) | [weights](https://drive.google.com/file/d/14zPRaYxMOe7hXi6N-Vs_QbWs6ue_CZPd/view?usp=sharing) |
| | | | | | | |
| **YOLOv4**pacsp-s | 640 | 37.0% | 55.7% | 40.0% | 20.2% | 41.6% | 48.4% | [cfg](https://github.com/WongKinYiu/PyTorch_YOLOv4/blob/master/cfg/yolov4-pacsp-s.cfg) | [weights](https://drive.google.com/file/d/1PiS9pF4tsydPN4-vMjiJPHjIOJMeRwWS/view?usp=sharing) |
| **YOLOv4**pacsp | 640 | 47.7% | 66.4% | 52.0% | 32.3% | 53.0% | 61.7% | [cfg](https://github.com/WongKinYiu/PyTorch_YOLOv4/blob/master/cfg/yolov4-pacsp.cfg) | [weights](https://drive.google.com/file/d/1C7xwfYzPF4dKFAmDNCetdTCB_cPvsuwf/view?usp=sharing) |
| **YOLOv4**pacsp-x | 640 | **50.0%** | **68.3%** | **54.5%** | **33.9%** | **55.4%** | **63.7%** | [cfg](https://github.com/WongKinYiu/PyTorch_YOLOv4/blob/master/cfg/yolov4-pacsp-x.cfg) | [weights](https://drive.google.com/file/d/1kWzJk5DJNlW9Xf2xR89OfmrEoeY9Szzj/view?usp=sharing) |
| | | | | | | |
| **YOLOv4**pacsp-s-mish | 640 | 38.8% | 57.8% | 42.0% | 21.6% | 43.7% | 51.1% | [cfg](https://github.com/WongKinYiu/PyTorch_YOLOv4/blob/master/cfg/yolov4-pacsp-s-mish.cfg) | [weights](https://drive.google.com/file/d/1OiDhQqYH23GrP6f5vU2j_DvA8PqL0pcF/view?usp=sharing) |
| **YOLOv4**pacsp-mish | 640 | 48.8% | 67.2% | 53.4% | 31.5% | 54.4% | 62.2% | [cfg](https://github.com/WongKinYiu/PyTorch_YOLOv4/blob/master/cfg/yolov4-pacsp-mish.cfg) | [weights](https://drive.google.com/file/d/1mk9mkM0_B9e_QgPxF6pBIB6uXDxZENsk/view?usp=sharing) |
| **YOLOv4**pacsp-x-mish | 640 | 51.2% | 69.4% | 55.9% | 35.0% | 56.5% | 65.0% | [cfg](https://github.com/WongKinYiu/PyTorch_YOLOv4/blob/master/cfg/yolov4-pacsp-x-mish.cfg) | [weights](https://drive.google.com/file/d/1kZee29alFFnm1rlJieAyHzB3Niywew_0/view?usp=sharing) |
| 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](https://github.com/WongKinYiu/PyTorch_YOLOv4/blob/master/cfg/yolov4.cfg) | [weights](https://drive.google.com/file/d/137U-oLekAu-J-fe0E_seTblVxnU3tlNC/view?usp=sharing) |
| | | | | | | |
| **YOLOv4**pacsp-s | 672 | 36.6% | 55.5% | 39.6% | 21.2% | 41.1% | 47.0% | [cfg](https://github.com/WongKinYiu/PyTorch_YOLOv4/blob/master/cfg/yolov4-pacsp-s.cfg) | [weights](https://drive.google.com/file/d/1-QZc043NMNa_O0oLaB3r0XYKFRSktfsd/view?usp=sharing) |
| **YOLOv4**pacsp | 672 | 47.2% | 66.2% | 51.6% | 30.4% | 52.3% | 60.8% | [cfg](https://github.com/WongKinYiu/PyTorch_YOLOv4/blob/master/cfg/yolov4-pacsp.cfg) | [weights](https://drive.google.com/file/d/1sIpu29jEBZ3VI_1uy2Q1f3iEzvIpBZbP/view?usp=sharing) |
| **YOLOv4**pacsp-x | 672 | **49.3%** | **68.1%** | **53.6%** | **31.8%** | **54.5%** | **63.6%** | [cfg](https://github.com/WongKinYiu/PyTorch_YOLOv4/blob/master/cfg/yolov4-pacsp-x.cfg) | [weights](https://drive.google.com/file/d/1aZRfA2CD9SdIwmscbyp6rXZjGysDvaYv/view?usp=sharing) |
| | | | | | | |
| **YOLOv4**pacsp-s-mish | 672 | 38.6% | 57.7% | 41.8% | 22.3% | 43.5% | 49.3% | [cfg](https://github.com/WongKinYiu/PyTorch_YOLOv4/blob/master/cfg/yolov4-pacsp-s-mish.cfg) | [weights](https://drive.google.com/file/d/1q0zbQKcSNSf_AxWQv6DAUPXeaTywPqVB/view?usp=sharing) |
| (+BoF) | 640 | 39.9% | 59.1% | 43.1% | 24.4% | 45.2% | 51.4% | | [weights](https://drive.google.com/file/d/1-8PqBaI8oYb7TB9L-KMzvjZcK_VaGXCF/view?usp=sharing) |
| **YOLOv4**pacsp-mish | 672 | 48.1% | 66.9% | 52.3% | 30.8% | 53.4% | 61.7% | [cfg](https://github.com/WongKinYiu/PyTorch_YOLOv4/blob/master/cfg/yolov4-pacsp-mish.cfg) | [weights](https://drive.google.com/file/d/116yreAUTK_dTJErDuDVX2WTIBcd5YPSI/view?usp=sharing) |
| (+BoF) | 640 | 49.3% | 68.2% | 53.8% | 31.9% | 54.9% | 62.8% | | [weights](https://drive.google.com/file/d/12qRrqDRlUElsR_TI97j4qkrttrNKKG3k/view?usp=sharing) |
| **YOLOv4**pacsp-x-mish | 672 | 50.0% | 68.5% | 54.4% | 32.9% | 54.9% | 64.0% | [cfg](https://github.com/WongKinYiu/PyTorch_YOLOv4/blob/master/cfg/yolov4-pacsp-x-mish.cfg) | [weights](https://drive.google.com/file/d/1GGCrokkRZ06CZ5MUCVokbX1FF2e1DbPF/view?usp=sharing) |
| (+BoF) | 640 | **51.0%** | **69.7%** | **55.5%** | **33.3%** | **56.2%** | **65.5%** | | [weights](https://drive.google.com/file/d/1lVmSqItSKywg6yk1qiCvgOYw55O03Qgj/view?usp=sharing) |
| | | | | | | |
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}
}
Acknowledgements