Unofficial Tensorflow 2 implementation of the Barlow Twins Self-Supervised Learning method
$ python train.py --name my_test /data
$ python train.py --helpmodel = barlow_twins.BarlowTwinsModel(input_height=224,
input_width=224,
projection_units=8192,
drop_projection_layer=True)
model.load_weights(saved_weights, by_name=True)
# Input image values should be in range [0, 255] --> preprocessing is built into the model
embedding = model(image)Convergence (Oxford 102 Flowers
pip install -r requirements.txtBuild
docker build -t barlow .Run a training
docker run --rm \
-t \
-u $(id -u):$(id -g) \
--gpus all \
-v $(pwd):/code \
-v <DATASET_FOLDER_PATH>:/data \
-w /code \
barlow \
python train.py --name my_test /data@article{DBLP:journals/corr/abs-2103-03230,
author = {Jure Zbontar and Li Jing and Ishan Misra and Yann LeCun and St{\'{e}}phane Deny},
title = {Barlow Twins: Self-Supervised Learning via Redundancy Reduction},
journal = {CoRR},
volume = {abs/2103.03230},
year = {2021},
url = {https://arxiv.org/abs/2103.03230},
archivePrefix = {arXiv},
eprint = {2103.03230},
timestamp = {Mon, 15 Mar 2021 17:30:55 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2103-03230.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}- Evaluation
- Linear evaluation
- KNN eval
- Choose or use custom backbone
- Save model
- Save only when loss improved