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ijepa

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 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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-## Model Details
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 ---
 library_name: transformers
+license: cc-by-nc-4.0
+datasets:
+- ILSVRC/imagenet-1k
 ---
+# 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.
+![ijepa](https://github.com/facebookresearch/ijepa/assets/7530871/dbad94ab-ac35-433b-8b4c-ca227886d311)
+## 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).
+![Illustrating how the predictor learns to model the semantics of the world](https://github.com/facebookresearch/ijepa/assets/7530871/9b66e461-fc8b-4b12-9f06-63ec4dfc1452)
+## 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}
+}
+```