- Download & Extract YOLOv7(pose): YOLOv7 (Branches: pose)
- Download Anaconda: Anaconda
- Environment: anaconda (window, wsl, linux)
- Get YOLOv7 inference code and download yolov7-w6-pose.pt
- Create new environment:
conda update conda
conda create --name my_env
conda info --env
conda activate my_env
Install CuDNN
conda install -c anaconda cudnn
Install Tensorflow
conda install -c conda-forge tensorflow
Install Pytorch
conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.3 -c pytorch
After install Pytorch, command:
python
import torch
torch.cuda.is_available()
Desired results:
True
After that, exit:
exit()
Download file requirements.txt and command
pip install -r requirements.txt
Test Pose estimation with yolov7-w6-pose.pt
python detect.py --weight yolov7-w6-pose.pt --kpt-label --hide-labels --hide-conf --source <path> --line-thickness <int> --nosave --view-img
Usage
detect.py [-h] [--weights WEIGHTS [WEIGHTS ...]] [--source SOURCE] [--img-size IMG_SIZE [IMG_SIZE ...]]
[--conf-thres CONF_THRES] [--iou-thres IOU_THRES] [--device DEVICE] [--view-img] [--save-txt]
[--save-txt-tidl] [--save-bin] [--save-conf] [--save-crop] [--nosave]
[--classes CLASSES [CLASSES ...]] [--agnostic-nms] [--augment] [--update] [--project PROJECT]
[--name NAME] [--exist-ok] [--line-thickness LINE_THICKNESS] [--hide-labels] [--hide-conf]
[--kpt-label] [--nobbox]
Real Time Pose Estimation
python detect.py --weight yolov7-w6-pose.pt --kpt-label --hide-labels --hide-conf --source 0 --nosave --view-img
Note: You can get YOLOv7 inference code and download difference WEIGHTS
python detect.py --<WEIGHTS> --kpt-label --hide-labels --hide-conf --source <path> --nobbox
- Youtube: Official YOLO V7 Pose Estimate | Windows and Linux
- Dataset: Click here to download