yolor下载 - yolor源代码下载

yolor

Python

1.0.0

下载

Yolor

纸的实施 - 您只学习一个表示：多个任务的统一网络

统一网络

要在表格上获取结果，请使用此分支。

模型	测试尺寸	AP^测试	AP ₅₀^测试	AP ₇₅^测试	batch1吞吐量	batch32推理
yolor-csp	640	52.8％	71.2％	57.6％	106 fps	3.2 ms
yolor-csp-x	640	54.8％	73.1％	59.7％	87 fps	5.5 ms
Yolor-P6	1280	55.7％	73.3％	61.0％	76 fps	8.3 ms
Yolor-W6	1280	56.9％	74.4％	62.2％	66 fps	10.7 ms
Yolor-E6	1280	57.6％	75.2％	63.0％	45 fps	17.1 ms
Yolor-D6	1280	58.2％	75.8％	63.8％	34 fps	21.8毫秒

Yolov4-P5	896	51.8％	70.3％	56.6％	41 fps （旧）	-
Yolov4-P6	1280	54.5％	72.6％	59.8％	30 fps （旧）	-
Yolov4-P7	1536年	55.5％	73.4％	60.8％	16 fps （旧）	-

在我们的NFS上修复速度瓶颈，这要归功于NCHC，TWCC和Narlabs支持团队。

模型	测试尺寸	AP ^Val	AP ₅₀ ^Val	AP ₇₅ ^Val	Ap _S ^Val	Ap _M ^Val	Ap _L ^Val	权重
yolov4-csp	640	49.1％	67.7％	53.8％	32.1％	54.4％	63.2％	-
yolor-csp	640	49.2％	67.6％	53.7％	32.9％	54.4％	63.0％	权重
Yolor-CSP *	640	50.0％	68.7％	54.3％	34.2％	55.1％	64.3％	权重

yolov4-csp-x	640	50.9％	69.3％	55.4％	35.3％	55.8％	64.8％	-
yolor-csp-x	640	51.1％	69.6％	55.7％	35.7％	56.0％	65.2％	权重
yolor-csp-x *	640	51.5％	69.9％	56.1％	35.8％	56.8％	66.1％	权重

发展...

模型	测试尺寸	AP^测试	AP ₅₀^测试	AP ₇₅^测试	AP_的^测试	AP _M^测试	AP _L^测试
yolor-csp	640	51.1％	69.6％	55.7％	31.7％	55.3％	64.7％
yolor-csp-x	640	53.0％	71.4％	57.9％	33.7％	57.1％	66.8％

从头开始训练300个时代...

模型	信息	测试尺寸	AP
yolor-csp	进化	640	48.0％
yolor-csp	战略	640	50.0％
yolor-csp	策略 + Simota	640	51.1％

yolor-csp-x	战略	640	51.5％
yolor-csp-x	策略 + Simota	640	53.0％

安装

Docker环境（推荐）

扩张

 # create the docker container, you can change the share memory size if you have more.
nvidia-docker run --name yolor -it -v your_coco_path/:/coco/ -v your_code_path/:/yolor --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

# install pytorch_wavelets if you want to use dwt down-sampling module
# https://github.com/fbcotter/pytorch_wavelets
cd /
git clone https://github.com/fbcotter/pytorch_wavelets
cd pytorch_wavelets
pip install .

# go to code folder
cd /yolor

COLAB环境

扩张

 git clone https://github.com/WongKinYiu/yolor
cd yolor

# pip install required packages
pip install -qr requirements.txt

# install mish-cuda if you want to use mish activation
# https://github.com/thomasbrandon/mish-cuda
# https://github.com/JunnYu/mish-cuda
git clone https://github.com/JunnYu/mish-cuda
cd mish-cuda
python setup.py build install
cd ..

# install pytorch_wavelets if you want to use dwt down-sampling module
# https://github.com/fbcotter/pytorch_wavelets
git clone https://github.com/fbcotter/pytorch_wavelets
cd pytorch_wavelets
pip install .
cd ..

准备可可数据集

扩张

 cd /yolor
bash scripts/get_coco.sh

准备预处理的体重

扩张

 cd /yolor
bash scripts/get_pretrain.sh

测试

yolor_p6.pt

 python test.py --data data/coco.yaml --img 1280 --batch 32 --conf 0.001 --iou 0.65 --device 0 --cfg cfg/yolor_p6.cfg --weights yolor_p6.pt --name yolor_p6_val

您将获得结果：

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.52510
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.70718
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.57520
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.37058
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.56878
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.66102
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.39181
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.65229
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.71441
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.57755
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.75337
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.84013

训练

单GPU培训：

 python train.py --batch-size 8 --img 1280 1280 --data coco.yaml --cfg cfg/yolor_p6.cfg --weights '' --device 0 --name yolor_p6 --hyp hyp.scratch.1280.yaml --epochs 300

多次GPU培训：

 python -m torch.distributed.launch --nproc_per_node 2 --master_port 9527 train.py --batch-size 16 --img 1280 1280 --data coco.yaml --cfg cfg/yolor_p6.cfg --weights '' --device 0,1 --sync-bn --name yolor_p6 --hyp hyp.scratch.1280.yaml --epochs 300

论文中的培训时间表：

 python -m torch.distributed.launch --nproc_per_node 8 --master_port 9527 train.py --batch-size 64 --img 1280 1280 --data data/coco.yaml --cfg cfg/yolor_p6.cfg --weights '' --device 0,1,2,3,4,5,6,7 --sync-bn --name yolor_p6 --hyp hyp.scratch.1280.yaml --epochs 300
python -m torch.distributed.launch --nproc_per_node 8 --master_port 9527 tune.py --batch-size 64 --img 1280 1280 --data data/coco.yaml --cfg cfg/yolor_p6.cfg --weights 'runs/train/yolor_p6/weights/last_298.pt' --device 0,1,2,3,4,5,6,7 --sync-bn --name yolor_p6-tune --hyp hyp.finetune.1280.yaml --epochs 450
python -m torch.distributed.launch --nproc_per_node 8 --master_port 9527 train.py --batch-size 64 --img 1280 1280 --data data/coco.yaml --cfg cfg/yolor_p6.cfg --weights 'runs/train/yolor_p6-tune/weights/epoch_424.pt' --device 0,1,2,3,4,5,6,7 --sync-bn --name yolor_p6-fine --hyp hyp.finetune.1280.yaml --epochs 450

推理

yolor_p6.pt

 python detect.py --source inference/images/horses.jpg --cfg cfg/yolor_p6.cfg --weights yolor_p6.pt --conf 0.25 --img-size 1280 --device 0

您将获得结果：

引用

 @article{wang2023you,
  title={You Only Learn One Representation: Unified Network for Multiple Tasks},
  author={Wang, Chien-Yao and Yeh, I-Hau and Liao, Hong-Yuan Mark},
  journal={Journal of Information Science and Engineering},
  year={2023}
}