| ---
|
| 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](https://github.com/ParamThakkar123/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)](https://arxiv.org/abs/2301.08243)**.
|
|
|
| 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](https://paramthakkar123.github.io/simulusion/gallery.html).
|
|
|
| ## Usage
|
|
|
| ```bash
|
| pip install simulusion huggingface_hub
|
| ```
|
|
|
| ```python
|
| 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:
|
|
|
| ```bash
|
| python demos/jepa_demo.py train --epochs 30
|
| ```
|
|
|
| ## Citation
|
|
|
| If you use this model, please cite the original paper:
|
|
|
| ```bibtex
|
| @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:
|
|
|
| ```bibtex
|
| @software{simulusion,
|
| title = {Simulusion: a PyTorch library for world models},
|
| author = {Thakkar, Param},
|
| year = {2026},
|
| url = {https://github.com/ParamThakkar123/simulusion},
|
| }
|
| ```
|
|
|
