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* Adding cvt files * Adding cvt files * changes in init file * Adding cvt files * changes in init file * Style fixes * Address comments from code review * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Format lists in docstring * Fix copies * Apply suggestion from code review Co-authored-by: AnugunjNaman <anugunjjha@gmail.com> Co-authored-by: Ayushman Singh <singhayushman13@protonmail.com> Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
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<!--Copyright 2022 The HuggingFace Team. All rights reserved. | ||
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with | ||
the License. You may obtain a copy of the License at | ||
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http://www.apache.org/licenses/LICENSE-2.0 | ||
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on | ||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the | ||
specific language governing permissions and limitations under the License. | ||
--> | ||
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# Convolutional Vision Transformer (CvT) | ||
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## Overview | ||
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The CvT model was proposed in [CvT: Introducing Convolutions to Vision Transformers](https://arxiv.org/abs/2103.15808) by Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan and Lei Zhang. The Convolutional vision Transformer (CvT) improves the [Vision Transformer (ViT)](vit) in performance and efficiency by introducing convolutions into ViT to yield the best of both designs. | ||
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The abstract from the paper is the following: | ||
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*We present in this paper a new architecture, named Convolutional vision Transformer (CvT), that improves Vision Transformer (ViT) | ||
in performance and efficiency by introducing convolutions into ViT to yield the best of both designs. This is accomplished through | ||
two primary modifications: a hierarchy of Transformers containing a new convolutional token embedding, and a convolutional Transformer | ||
block leveraging a convolutional projection. These changes introduce desirable properties of convolutional neural networks (CNNs) | ||
to the ViT architecture (\ie shift, scale, and distortion invariance) while maintaining the merits of Transformers (\ie dynamic attention, | ||
global context, and better generalization). We validate CvT by conducting extensive experiments, showing that this approach achieves | ||
state-of-the-art performance over other Vision Transformers and ResNets on ImageNet-1k, with fewer parameters and lower FLOPs. In addition, | ||
performance gains are maintained when pretrained on larger datasets (\eg ImageNet-22k) and fine-tuned to downstream tasks. Pre-trained on | ||
ImageNet-22k, our CvT-W24 obtains a top-1 accuracy of 87.7\% on the ImageNet-1k val set. Finally, our results show that the positional encoding, | ||
a crucial component in existing Vision Transformers, can be safely removed in our model, simplifying the design for higher resolution vision tasks.* | ||
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Tips: | ||
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- CvT models are regular Vision Transformers, but trained with convolutions. They outperform the [original model (ViT)](vit) when fine-tuned on ImageNet-1K and CIFAR-100. | ||
- You can check out demo notebooks regarding inference as well as fine-tuning on custom data [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/VisionTransformer) (you can just replace [`ViTFeatureExtractor`] by [`AutoFeatureExtractor`] and [`ViTForImageClassification`] by [`CvtForImageClassification`]). | ||
- The available checkpoints are either (1) pre-trained on [ImageNet-22k](http://www.image-net.org/) (a collection of 14 million images and 22k classes) only, (2) also fine-tuned on ImageNet-22k or (3) also fine-tuned on [ImageNet-1k](http://www.image-net.org/challenges/LSVRC/2012/) (also referred to as ILSVRC 2012, a collection of 1.3 million | ||
images and 1,000 classes). | ||
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This model was contributed by [anugunj](https://huggingface.co/anugunj). The original code can be found [here](https://github.com/microsoft/CvT). | ||
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## CvtConfig | ||
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[[autodoc]] CvtConfig | ||
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## CvtModel | ||
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[[autodoc]] CvtModel | ||
- forward | ||
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## CvtForImageClassification | ||
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[[autodoc]] CvtForImageClassification | ||
- forward |
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convnext, | ||
cpm, | ||
ctrl, | ||
cvt, | ||
data2vec, | ||
deberta, | ||
deberta_v2, | ||
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