| Name | jepa JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | https://github.com/dipsivenkatesh/jepa |
| Summary | Joint-Embedding Predictive Architecture for Self-Supervised Learning |
| upload_time | 2025-07-29 08:54:06 |
| maintainer | None |
| docs_url | None |
| author | Dilip Venkatesh |
| requires_python | >=3.8 |
| license | MIT License
Copyright (c) 2025 dipsivenkatesh
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
| keywords |
self-supervised-learning
representation-learning
deep-learning
pytorch
jepa
joint-embedding
predictive-architecture
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
torch
numpy
tqdm
transformers
datasets
scikit-learn
wandb
tensorboard
pyyaml
matplotlib
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# JEPA
A JEPA (Joint Embedding Predictive Architecture) training framework to train JEPAs.
## Features
- Modular model, data, trainer structure
- Easy to extend with new encoders/predictors
- CLI-based training and evaluation
## Getting Started
```bash
pip install -e .
jepa-train
```
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