yolov7下载yolov7源代码下载

yolov7

其他源码

1.0.0

下载

Yolov7官方

纸-Yolov7的实施：可训练的释放犬为实时对象探测器设置了新的最新最先进

网络演示

集成到拥抱面空间？使用Gradio。尝试网络演示

表现

可可女士

模型	测试尺寸	AP^测试	AP ₅₀^测试	AP ₇₅^测试	批次1 fps	批次32平均时间
Yolov7	640	51.4％	69.7％	55.9％	161 fps	2.8毫秒
Yolov7-X	640	53.1％	71.2％	57.8％	114 fps	4.3 ms

Yolov7-W6	1280	54.9％	72.6％	60.1％	84 fps	7.6毫秒
Yolov7-E6	1280	56.0％	73.5％	61.2％	56 fps	12.3 ms
Yolov7-D6	1280	56.6％	74.0％	61.8％	44 fps	15.0毫秒
yolov7-e6e	1280	56.8％	74.4％	62.1％	36 fps	18.7 ms

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 yolov7 -it -v your_coco_path/:/coco/ -v your_code_path/:/yolov7 --shm-size=64g nvcr.io/nvidia/pytorch:21.08-py3

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

# pip install required packages
pip install seaborn thop

# go to code folder
cd /yolov7

测试

yolov7.pt yolov7x.pt yolov7-w6.pt yolov7-e6.pt yolov7-d6.pt yolov7-e6e.pt

python test.py --data data/coco.yaml --img 640 --batch 32 --conf 0.001 --iou 0.65 --device 0 --weights yolov7.pt --name yolov7_640_val

您将获得结果：

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.51206
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.69730
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.55521
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.35247
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.55937
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.66693
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.38453
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.63765
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.68772
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.53766
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.73549
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.83868

要衡量准确性，请下载pycocotools的可可通知到./coco/annotations/instances_val2017.json

训练

数据准备

bash scripts/get_coco.sh

下载MS Coco数据集图像（火车，Val，测试）和标签。如果您以前使用过不同版本的Yolo，我们强烈建议您删除train2017.cache and val2017.cache Files，Redownload Labels

单GPU培训

 # train p5 models
python train.py --workers 8 --device 0 --batch-size 32 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights ' ' --name yolov7 --hyp data/hyp.scratch.p5.yaml

# train p6 models
python train_aux.py --workers 8 --device 0 --batch-size 16 --data data/coco.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6.yaml --weights ' ' --name yolov7-w6 --hyp data/hyp.scratch.p6.yaml

多次GPU培训

 # train p5 models
python -m torch.distributed.launch --nproc_per_node 4 --master_port 9527 train.py --workers 8 --device 0,1,2,3 --sync-bn --batch-size 128 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights ' ' --name yolov7 --hyp data/hyp.scratch.p5.yaml

# train p6 models
python -m torch.distributed.launch --nproc_per_node 8 --master_port 9527 train_aux.py --workers 8 --device 0,1,2,3,4,5,6,7 --sync-bn --batch-size 128 --data data/coco.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6.yaml --weights ' ' --name yolov7-w6 --hyp data/hyp.scratch.p6.yaml

转移学习

yolov7_training.pt yolov7x_training.pt yolov7-w6_training.pt yolov7-e6_training.pt yolov7-d6_training.pt yolov7-e6e_training.pt

自定义数据集的单个GPU登录

 # finetune p5 models
python train.py --workers 8 --device 0 --batch-size 32 --data data/custom.yaml --img 640 640 --cfg cfg/training/yolov7-custom.yaml --weights ' yolov7_training.pt ' --name yolov7-custom --hyp data/hyp.scratch.custom.yaml

# finetune p6 models
python train_aux.py --workers 8 --device 0 --batch-size 16 --data data/custom.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6-custom.yaml --weights ' yolov7-w6_training.pt ' --name yolov7-w6-custom --hyp data/hyp.scratch.custom.yaml

重新参数化

参见Reparameterization.ipynb

推理

在视频中：

python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source yourvideo.mp4

在图像：

python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source inference/images/horses.jpg

出口

pytorch至Coreml（以及MacOS/ios的推断）

用NMS（和推理）到Onnx的pytorch

python export.py --weights yolov7-tiny.pt --grid --end2end --simplify 
        --topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640 --max-wh 640

使用NMS（和推理）张力到张力

wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt
python export.py --weights ./yolov7-tiny.pt --grid --end2end --simplify --topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640
git clone https://github.com/Linaom1214/tensorrt-python.git
python ./tensorrt-python/export.py -o yolov7-tiny.onnx -e yolov7-tiny-nms.trt -p fp16

Pytorch以另一种方式张力

扩张

wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt
python export.py --weights yolov7-tiny.pt --grid --include-nms
git clone https://github.com/Linaom1214/tensorrt-python.git
python ./tensorrt-python/export.py -o yolov7-tiny.onnx -e yolov7-tiny-nms.trt -p fp16

# Or use trtexec to convert ONNX to TensorRT engine
/usr/src/tensorrt/bin/trtexec --onnx=yolov7-tiny.onnx --saveEngine=yolov7-tiny-nms.trt --fp16

测试：Python 3.7.13，Pytorch 1.12.0+CU113

姿势估计

code yolov7-w6-pose.pt

请参阅键 - ipynb。

实例分割（使用NTU）

code yolov7-mask.pt

请参阅instance.ipynb。

实例细分

code yolov7-seg.pt

yolov7例如分割（yolor + yolov5 + yolact）

模型	测试尺寸	AP^盒	AP ₅₀^盒	AP ₇₅^盒	AP^面具	AP ₅₀^口罩	AP ₇₅^口罩
yolov7-seg	640	51.4％	69.4％	55.8％	41.5％	65.5％	43.7％

锚自由检测头

code yolov7-u6.pt

Yolov7带有脱钩的TAL头（Yolor + Yolov5 + Yolov6）

模型	测试尺寸	AP ^Val	AP ₅₀ ^Val	AP ₇₅ ^Val
Yolov7-U6	640	52.6％	69.7％	57.3％

引用

 @inproceedings{wang2023yolov7,
  title={{YOLOv7}: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors},
  author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2023}
}

 @article{wang2023designing,
  title={Designing Network Design Strategies Through Gradient Path Analysis},
  author={Wang, Chien-Yao and Liao, Hong-Yuan Mark and Yeh, I-Hau},
  journal={Journal of Information Science and Engineering},
  year={2023}
}