This repository aims at mirroring popular semantic segmentation architectures in PyTorch.
- PSPNet - With support for loading pretrained models w/o caffe dependency
- ICNet - With optional batchnorm and pretrained models
- FRRN - Model A and B
- FCN - All 1 (FCN32s), 2 (FCN16s) and 3 (FCN8s) stream variants
- U-Net - With optional deconvolution and batchnorm
- Link-Net - With multiple resnet backends
- Segnet - With Unpooling using Maxpool indices
- pytorch >=0.4.0
- torchvision ==0.2.0
- visdom >=1.0.1 (for loss and results visualization)
- scipy
- tqdm
pip install -r requirements.txt
- Download data for desired dataset(s) from list of URLs here.
- Extract the zip / tar and modify the path appropriately in
config.json
Launch visdom by running (in a separate terminal window)
python -m visdom.server
To train the model :
python train.py [-h] [--config [CONFIG]]
--config Configuration file to use
To validate the model :
usage: validate.py [-h] [--config [CONFIG]] [--model_path [MODEL_PATH]]
[--eval_flip] [--measure_time]
--config Config file to be used
--model_path Path to the saved model
--eval_flip Enable evaluation with flipped image | True by default
--measure_time Enable evaluation with time (fps) measurement | True
by default
To test the model w.r.t. a dataset on custom images(s):
python test.py [-h] [--model_path [MODEL_PATH]] [--dataset [DATASET]]
[--dcrf [DCRF]] [--img_path [IMG_PATH]] [--out_path [OUT_PATH]]
--model_path Path to the saved model
--dataset Dataset to use ['pascal, camvid, ade20k etc']
--dcrf Enable DenseCRF based post-processing
--img_path Path of the input image
--out_path Path of the output segmap
If you find this code useful in your research, please consider citing:
@article{mshahsemseg,
Author = {Meet P Shah},
Title = {Semantic Segmentation Architectures Implemented in PyTorch.},
Journal = {https://github.com/meetshah1995/pytorch-semseg},
Year = {2017}
}
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent