Skip to content

Latest commit

 

History

History
57 lines (37 loc) · 1.69 KB

README.md

File metadata and controls

57 lines (37 loc) · 1.69 KB

Fine-tuning-JEPA

Simple I-JEPA implementation for pre-training and fine-tuning your own datasets.

Getting Started

Installation

git clone https://github.com/TSTB-dev/Fine-tuning-JEPA && cd Fine-tuning-JEPA

pip install -r requirements.txt

Setup datasets & Pre-trained checkpoints

For downloading datasets (supported datasets are "stanford-cars", "flowers", "oxford pets", "cub200-2011", "caltech101").

bash scripts/setup_dataset.sh

For downloading checkpoints (from official) bash scripts/download_checkpoints.sh

For Pre-Training

Please modify the content in the script and config files, then run following command.

bash scripts/pretrain.sh

For Downstream Training

We provide 2 different trainig strategies: Linear-probing, Full fine tuning. Please modify the content in the script and config files, then run following command.

bash scripts/downstream.sh

For Evaluation

Please modify the content in the script and config files, then run following command.

bash scripts/evaluation.sh

Main Experimental Results

We provide some experimental results on popular vision datasets. All experiments use IN-1k pre-trained I-JEPA model (in1k_vith14_ep300) which is officially provided.

Dataset Linear Probing Full fine-tuning
pets 89.2 [98.7] 83.6 [100]
stanford_cars 25.6 [58.6] 10.94 [99.8]
flowers102 82.4 [98.7] 90.80 [100.0]
caltech101 93.5 [97.3] 92.60 [99.62]
cub200 42.51 [85.10] 30.20 [99.97]

We report test acc. [train acc.] in each value in this table.