Implementation of I-JEPA from "Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture"

PyTorch implementation of Semi-supervised Vision Transformers

Fork of I-JEPA, the Image-based Joint-Embedding Predictive Architecture. First outlined in the CVPR paper, "Self-supervised learning from images with a joint-embedding predictive architecture."

I-JEPA finetuning recipe

VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.

ImageNet-1K data download, processing for using as a dataset

Official codebase for I-JEPA, the Image-based Joint-Embedding Predictive Architecture. First outlined in the CVPR paper, "Self-supervised learning from images with a joint-embedding predictive arch…

PyTorch implementation of MAE https//arxiv.org/abs/2111.06377