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https://github.com/jfzhuang/IFR
[CVPR'22] Semi-Supervised Video Semantic Segmentation with Inter-Frame Feature Reconstruction
https://github.com/jfzhuang/IFR
cvpr2022 semantic-segmentation semi-supervised-learning semi-supervised-segmentation
Last synced: about 1 month ago
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[CVPR'22] Semi-Supervised Video Semantic Segmentation with Inter-Frame Feature Reconstruction
- Host: GitHub
- URL: https://github.com/jfzhuang/IFR
- Owner: jfzhuang
- License: mit
- Created: 2021-11-22T07:53:14.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2022-10-17T10:29:53.000Z (about 2 years ago)
- Last Synced: 2024-08-03T01:11:49.036Z (5 months ago)
- Topics: cvpr2022, semantic-segmentation, semi-supervised-learning, semi-supervised-segmentation
- Language: Python
- Homepage:
- Size: 19.7 MB
- Stars: 27
- Watchers: 2
- Forks: 4
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Semi-Supervised-Semantic-Segmentation - Code - Supervised_Video_Semantic_Segmentation_With_Inter-Frame_Feature_Reconstruction_CVPR_2022_paper.pdf)| (2022)
README
# IFR
This repository is the official implementation of "Semi-Supervised Video Semantic Segmentation with Inter-Frame Feature Reconstruction" (accepted by CVPR 2022). It is designed for semi-supervised video semantic segmentation task.
## Install & Requirements
The code has been tested on pytorch=1.8.2 and python3.8. Please refer to `requirements.txt` for detailed information.
**To Install python packages**
```
pip install -r requirements.txt
```
## Download Pretrained Weights
````bash
mkdir ./IFR/pretrained
cd ./IFR/pretrained
# download resnet18 imagenet pretrained weight
wget http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet18-imagenet.pth
# download resnet101 imagenet pretrained weight
wget http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet101-imagenet.pth
````
## Data preparation
You need to download the [Cityscapes](https://www.cityscapes-dataset.com/) datasets.
Your directory tree should be look like this:
````bash
./IFR/data
├── cityscapes
│ ├── gtFine
│ │ ├── train
│ │ └── val
│ └── leftImg8bit_sequence
│ ├── train
│ └── val
````
## Prepare Downsample Dataset
Generated downsample dataset would be saved in ./data
````bash
cd ./IFR
python tools/data_downsample.py
````
## Stage One Training of Accel
For example, train image segmentation model on 2 GPUs. Checkpoints would be saved in ./IFR/work_dirs.
````bash
# train PSP18 baseline model
cd ./IFR/exp/sup_30_res18/scripts
bash train.sh
# train PSP101 baseline model
cd ./IFR/exp/sup_30_res101/scripts
bash train.sh
# train PSP18 IFR model
cd ./IFR/exp/IFR_30_res18/scripts
bash train.sh
# train PSP101 IFR model
cd ./IFR/exp/IFR_30_res101/scripts
bash train.sh
````
## Stage Two Training of Accel
For example, train Accel18 on 2 GPUs. Checkpoints would be saved in ./Accel/work_dirs.
````bash
mkdir ./Accel/work_dirs
# train Accel18 with baseline model
cd ./Accel/exp/accel18_30_sup/script
bash train.sh
# train Accel18 with IFR model
cd ./Accel/exp/accel18_30_IFR/script
bash train.sh
````