https://github.com/bgu-cs-vil/spacejam

SpaceJAM: a Lightweight and Regularization-free Method for Fast Joint Alignment of Images (ECCV 2024)
https://github.com/bgu-cs-vil/spacejam

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SpaceJAM: a Lightweight and Regularization-free Method for Fast Joint Alignment of Images (ECCV 2024)

• Host: GitHub
• URL: https://github.com/bgu-cs-vil/spacejam
• Owner: BGU-CS-VIL
• License: mit
• Created: 2024-07-06T10:43:25.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-11-09T11:00:05.000Z (11 days ago)
• Last Synced: 2024-11-09T12:16:46.979Z (11 days ago)
• Language: Python
• Homepage: https://bgu-cs-vil.github.io/SpaceJAM/
• Size: 13.4 MB
• Stars: 18
• Watchers: 4
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# SpaceJAM: a Lightweight and Regularization-free Method for Fast Joint Alignment of Images (ECCV 2024)

Nir Barel*, Ron Shapira Weber*, Nir Mualem, Shahaf Finder and Oren Ferifeld

[![arXiv](https://img.shields.io/badge/arXiv-2407.05848-b31b1b.svg?style=flat)](https://arxiv.org/abs/2407.11850)

[![Project Page](https://img.shields.io/badge/Project-Page-blue)](https://bgu-cs-vil.github.io/SpaceJAM/)

![Introduction Figure](images/intro_fig.png)

*Our framework jointly aligns a set of images of an object category in only a few minutes.

Top-to-bottom: 1) input images; 2) learned low-dimensional representations; 3) aligned features; 4) aligned images.

The last column depicts the average representation (atlas) obtained after training.*

## Requirements

To set up the environment for this project, you need to install the required dependencies listed in `environment.yml`. This file specifies the necessary packages and channels to ensure that your environment is properly configured.

 1. Install Conda: If you don't have Conda installed, you can get it by installing Miniconda or Anaconda. Download Miniconda or Download Anaconda.

 2. Create the Environment: To create a Conda environment with the dependencies specified in `environment.yml`, use the following command:

```bash

conda env create -f environment.yml

```

 3. Activate the Environment: Once the environment is created, activate it using:

```bash

conda activate spacejam

```

### Download data 

You can download and preprocess the following datasets according to the paper: 

```bash

python prepare_data/prepare_spair.py

python prepare_data/prepare_cub_class.py [--cub_acsm_class ]

python prepare_data/prepare_cub_subsets.py

```

In order to run SpaceJAM on custom images set, use:

```bash

python prepare_data/prepare_image_set.py --path  [--out ] 

```

### How to run

To train the entire model on one of the dataset, simply run:

```bash

python train.py --data_folder 

```

Please note that it is assumes that the data resides at ```data_folder/images```. 

For more details, run:

```bash

python train.py --help

```

## Cite

```

@inproceedings{Barel:ECCV:2024:SpaceJAM,

      title={{SpaceJAM}: a Lightweight and Regularization-free Method for Fast Joint Alignment of Images},

      author={Nir Barel and Ron Shapira Weber and Nir Mualem and Shahaf E. Finder and Oren Freifeld},

      year={2024},

      booktitle={European Conference on Computer Vision},

}

```