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https://github.com/jfzhuang/DMR

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Host: GitHub
URL: https://github.com/jfzhuang/DMR
Owner: jfzhuang
License: mit
Created: 2021-09-07T13:31:51.000Z (about 3 years ago)
Default Branch: main
Last Pushed: 2023-07-31T08:33:45.000Z (about 1 year ago)
Last Synced: 2024-04-10T06:06:24.551Z (6 months ago)
Language: Python
Size: 929 KB
Stars: 7
Watchers: 1
Forks: 1
Open Issues: 2
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

# Seeing via Contexts and Experiences: Dual Memory-Guided Refinements for Video Semantic Segmentation
This repository is the official implementation of "Seeing via Contexts and Experiences: Dual Memory-Guided Refinements for Video Semantic Segmentation".

## Install & Requirements
The code has been tested on pytorch=1.8.1 and python3.7. Please refer to `requirements.txt` for detailed information.

**To Install python packages**
```
pip install -r requirements.txt
```
## Data preparation
You need to download the [Cityscapes](https://www.cityscapes-dataset.com/) and [CamVid](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/) datasets.

Your directory tree should be look like this:
````bash
$DAVSS_ROOT/data
├── cityscapes
│   ├── gtFine
│   │   ├── train
│   │   └── val
│   └── leftImg8bit_sequence
│      ├── train
│   └── val
├── camvid
│   ├── label
│   │   ├── segmentation annotations
│   └── video_image
│   ├── 0001TP
│   ├── decoded images from video clips
│   ├── 0006R0
│   └── 0016E5
│   └── Seq05VD
````
## Train and test
For example, train our proposed method on Cityscapes on 4 GPUs:
````bash
# train CRM
cd DMR/exp/cityscapes/psp50/CRM/script
bash train.sh
# generate experience-based memory bank
cd DMR/exp/cityscapes/psp50/generate_memory/script
bash test.sh
# train ERM
cd DMR/exp/cityscapes/psp50/ERM/script
bash train.sh
````

For example, test our proposed method on Cityscapes validation set:
````bash
cd DMR/exp/cityscapes/psp50/ERM/script
bash test.sh
````