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---
datasets:
- ILSVRC/imagenet-1k
library_name: transformers
license: cc-by-nc-4.0
---
# I-JEPA Model (Huge, fine-tuned on IN1K)
**I-JEPA** is a method for self-supervised learning. At a high level, I-JEPA predicts the representations of part of an image from the representations of other parts of the same image:
1. without relying on pre-specified invariances to hand-crafted data transformations, which tend to be biased for particular downstream tasks,
2. and without having the model fill in pixel-level details, which tend to result in learning less semantically meaningful representations.
## How does it work?
As opposed to generative methods that have a pixel decoder, I-JEPA has a predictor that makes predictions in latent space.
The predictor in I-JEPA can be seen as a primitive (and restricted) world-model that is able to model spatial uncertainty in a static image from a partially observable context.
This world model is semantic in the sense that it predicts high level information about unseen regions in the image, rather than pixel-level details.
We trained a stochastic decoder that maps the I-JEPA predicted representations back in pixel space as sketches.
The model correctly captures positional uncertainty and produces high-level object parts with the correct pose (e.g., dog’s head, wolf’s front legs).
## Intended uses & limitations
I-JEPA can be used for image classification or feature extraction. This checkpoint in specific is intended for **Feature Extraction**.
### BibTeX entry and citation info
If you use I-JEPA or this code in your work, please cite:
```
@article{assran2023self,
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},
journal={arXiv preprint arXiv:2301.08243},
year={2023}
}
``` |