library_name: simulusion
license: mit
pipeline_tag: image-feature-extraction
datasets:
- uoft-cs/cifar10
tags:
- simulusion
- pytorch
- ijepa
- jepa
- self-supervised-learning
- vision-transformer
- cifar10
I-JEPA ViT-Tiny on CIFAR-10
A demo checkpoint from Simulusion, an open-source PyTorch library that puts world models behind one API. It implements the paper Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture (Assran et al., CVPR 2023).
I-JEPA learns image features without labels and without reconstructing pixels: it predicts the representations of masked image blocks from a visible context block. In Simulusion it is a representation-learning component for world models.
Results
A ViT-Tiny (patch size 4) trained for 30 epochs on CIFAR-10, about an hour on a 4 GB RTX 3050 laptop GPU. Classifying test images by their 20 nearest training images in feature space scores 41.1%, against 29.6% for the same network at random initialisation (chance is 10%).
This is a short demo run sized for a laptop GPU, meant to show what the model does. It is not a reproduction of the paper's results. More clips and learning curves are in the gallery.
Usage
pip install simulusion huggingface_hub
from simulusion.models.jepa_agent import JEPAAgent
agent = JEPAAgent.from_pretrained("simillusion/ijepa-vit-tiny-cifar10")
Checkpoints are plain state dicts and load with torch.load(..., weights_only=True). They include optimizer state, so you can continue training from them.
Training
Reproduce this checkpoint from a clone of the repository:
python demos/jepa_demo.py train --epochs 30
Citation
If you use this model, please cite the original paper:
@inproceedings{assran2023ijepa,
title = {Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture},
author = {Assran, Mahmoud and Duval, Quentin and Misra, Ishan and Bojanowski, Piotr and Vincent, Pascal and Rabbat, Michael and LeCun, Yann and Ballas, Nicolas},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2023},
url = {https://arxiv.org/abs/2301.08243},
}
and Simulusion:
@software{simulusion,
title = {Simulusion: a PyTorch library for world models},
author = {Thakkar, Param},
year = {2026},
url = {https://github.com/ParamThakkar123/simulusion},
}