🚀 DINO ResNet-50
DINO ResNet-50 是使用 DINO 進行預訓練的 ResNet-50 模型。DINO 出自論文 Emerging Properties in Self-Supervised Vision Transformers,而 ResNet 則出自 Deep Residual Learning for Image Recognition。DINO ResNet-50 的官方實現可在 此處 找到。
本項目的權重是使用 此腳本 從官方 DINO ResNet 轉換而來。
有關最新的模型卡片信息,請參閱 原始倉庫。
🚀 快速開始
模型使用
⚠️ 重要提示
本倉庫中的特徵提取器是從 microsoft/resnet-50
複製而來。我們尚未驗證此圖像預處理方法是否適用於 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 引用信息
@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}
}
📄 信息表格
屬性 |
詳情 |
標籤 |
dino、vision |
數據集 |
imagenet-1k |