http://cvlab.cse.msu.edu/pdfs/Zhang_Tran_Yin_Atoum_Liu_Wan_Wang_CVPR2019.pdf
- Linux/ Windows
- Anaconda 3 - Python 3.7 version
Raw_Dataset http://cvlab.cse.msu.edu/frontal-view-gaitfvg-database.html
- Download codes
cd GaitNet/
- Install required libs
pip install -r requirements.txt
- OR if you use Anaconda
- Download Dataset cropped images
Unzip to Data/ , do not change the three folder names
The final folder structure should look like:
- Data
- SEG-S1
- SEG-S2
- SEG-S3
- GaitNet
- train.py
- runs
- ...
Since the code is set up with orginal papers defaut hyperparameters, simply run with:
python train.py
You will be asked which GPU to use, enter 0 if you have only one GPU. If you have multiple GPUs, check their index with
nvidia-smi
After running train.py, run TensorboardX to visulize training loss curves and synthesized results:
tensorboard --logdir runs
MRCNN: TORCHVISION
IMAGE PROCESSING: PIL, TORCHVISION
- MASK R-CNN
- torchvision
- faster (11-12FPS on 1080P)
- threading data loader
- more effective algorithm to remove redundant data(out of frame)
- CUDNN
- 3 times fater for training