DINO ResNet-50
ResNet-50 pretrained with DINO. DINO was introduced in Emerging Properties in Self-Supervised Vision Transformers, while ResNet was introduced in Deep Residual Learning for Image Recognition. The official implementation of a DINO Resnet-50 can be found here.
Weights converted from the official DINO 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 DINO 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/dino-resnet-50')
model = ResNetModel.from_pretrained('Ramos-Ramos/dino-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{DBLP:journals/corr/abs-2104-14294,
author = {Mathilde Caron and
Hugo Touvron and
Ishan Misra and
Herv{\'{e}} J{\'{e}}gou and
Julien Mairal and
Piotr Bojanowski and
Armand Joulin},
title = {Emerging Properties in Self-Supervised Vision Transformers},
journal = {CoRR},
volume = {abs/2104.14294},
year = {2021},
url = {https://arxiv.org/abs/2104.14294},
archivePrefix = {arXiv},
eprint = {2104.14294},
timestamp = {Tue, 04 May 2021 15:12:43 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2104-14294.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@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}
}