VICReg ResNet-50
ResNet-50 pretrained with VICReg. VICReg was introduced in VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning, while ResNet was introduced in Deep Residual Learning for Image Recognition. The official implementation of a VICReg Resnet-50 can be found here.
Weights converted from the official VICReg ResNet using this script.
For up-to-date model card information, please see the original repo.
How to use
Warning: The feature extractor in this repo is a copy of the one from microsoft/resnet-50
. We never verified if this image prerprocessing is the one used with VICReg ResNet-50.
from transformers import AutoFeatureExtractor, ResNetModel
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = AutoFeatureExtractor.from_pretrained('Ramos-Ramos/vicreg-resnet-50')
model = ResNetModel.from_pretrained('Ramos-Ramos/vicreg-resnet-50')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
BibTeX entry and citation info
@article{bardes2021vicreg,
title={Vicreg: Variance-invariance-covariance regularization for self-supervised learning},
author={Bardes, Adrien and Ponce, Jean and LeCun, Yann},
journal={arXiv preprint arXiv:2105.04906},
year={2021}
}
@inproceedings{he2016deep,
title={Deep residual learning for image recognition},
author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={770--778},
year={2016}
}
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