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https://github.com/hasibzunair/mlcv-readme-template
A template README/workflow for any ML based research project.
https://github.com/hasibzunair/mlcv-readme-template
Last synced: about 2 months ago
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A template README/workflow for any ML based research project.
- Host: GitHub
- URL: https://github.com/hasibzunair/mlcv-readme-template
- Owner: hasibzunair
- Created: 2023-03-22T16:38:08.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-24T19:28:04.000Z (5 months ago)
- Last Synced: 2024-07-24T22:25:28.285Z (5 months ago)
- Homepage:
- Size: 33.2 KB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Project Acronym
**Concordia University, Decathlon**
Hasib Zunair
[[`Paper`](link)] [[`Project`](link)] [[`Demo`](#4-demo)] [[`BibTeX`](#5-citation)]
This is official code for our **BMVC 2022 paper**:
[Title of Your Paper](Link)
Summarize in 3-5 sentences your project here.
## 1. Specification of dependencies
This code requires Python YOUR_PYTHON_VERSION and CUDA YOUR_CUDA_VERSION. Clone the project repository, then create and activate the following conda envrionment.
```bash
# clone repo
git clone https://github.com/hasibzunair/mlcv-readme-template
cd peekaboo
# create env
conda update conda
conda env create -f environment.yml
conda activate myenv
```
Or, you can also create a fresh environment and install the project requirements inside that environment by:
```bash
# clone repo
git clone https://github.com/hasibzunair/mlcv-readme-template
cd peekaboo
# create fresh env
conda create -n myenv python=3.8
conda activate myenv
# install reqs
pip install -r requirements.txt
```
## 2a. Training code
### Dataset details
We expect Dataset1 and Dataset2 datasets to have the following structure:
```bash
datasets/
|-- Dataset1/
|---- VOC2007/
|------ JPEGImages/
|------ Annotations/
|------ ImageSets/
......
|-- Dataset2/
|---- annotations/
|---- images/
|------ train2014/
|------ val2014/
...
```
Add any intructions for pre-processing data, to make it ready for training.
### Dataset1 training
```bash
python train.py --dataset Dataset1
```
### Dataset2 training
```bash
python train.py --dataset Dataset2
```
## 2b. Evaluation code
### Dataset1 eval
```bash
python eval.py --dataset Dataset1
```
### Dataset2 eval
```bash
python eval.py --dataset Dataset2
```
Refer to supplementary materials if any.
## 3. Pre-trained models
We provide pretrained models on [GitHub Releases](https://github.com/hasibzunair/masksup-segmentation/releases/tag/v0.1) for reproducibility.
|Dataset | Backbone | mIoU(%) | Download |
| ---------- | ------- | ------ | -------- |
| GLaS |LeViT-UNet 384 | 76.06 | [download](https://github.com/hasibzunair/masksup-segmentation/releases/download/v0.1/masksupglas76.06iou.pth) |
| Kvasir & CVC-ClinicDB |LeViT-UNet 384 | 84.02 | [download](https://github.com/hasibzunair/masksup-segmentation/releases/download/v0.1/masksuppolyp84.02iou.pth) |
| NYUDv2 |U-Net++ | 39.31 | [download](https://github.com/hasibzunair/masksup-segmentation/releases/download/v0.1/masksupnyu39.31iou.pth) |
## 4. Demo
Add demo details here.
## 5. Citation
If you use X in your research, please use the following BibTeX entry.
```bibtex
@inproceedings{zunair2022masked,
title={Masked Supervised Learning for Semantic Segmentation},
author={Zunair, Hasib and Hamza, A Ben},
booktitle={Proc. British Machine Vision Conference},
year={2022}
}
```
## Project Notes
Click to view
**[July 12, 2022]** Added this fancy drop down!
**[March 22, 2022]** I started making this template for my own reference.
## Acknowledgements
Give credits to codebases you built on!
### Useful resources
* [Reproducibility Checklist](https://www.cs.mcgill.ca/~jpineau/ReproducibilityChecklist.pdf)
* [Tips for releasing research code in Machine Learning (with official NeurIPS 2020 recommendations)](https://github.com/paperswithcode/releasing-research-code)