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320 lines
9.2 KiB
320 lines
9.2 KiB
# Vision Transformer (ViT)
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The **Vision Transformer** is a model for image classification that employs a Transformer-like architecture over patches of the image. This includes the use of [Multi-Head Attention](https://paperswithcode.com/method/multi-head-attention), [Scaled Dot-Product Attention](https://paperswithcode.com/method/scaled) and other architectural features seen in the [Transformer](https://paperswithcode.com/method/transformer) architecture traditionally used for NLP.
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{% include 'code_snippets.md' %}
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## How do I train this model?
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You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
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## Citation
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```BibTeX
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@misc{dosovitskiy2020image,
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title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
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author={Alexey Dosovitskiy and Lucas Beyer and Alexander Kolesnikov and Dirk Weissenborn and Xiaohua Zhai and Thomas Unterthiner and Mostafa Dehghani and Matthias Minderer and Georg Heigold and Sylvain Gelly and Jakob Uszkoreit and Neil Houlsby},
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year={2020},
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eprint={2010.11929},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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<!--
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Type: model-index
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Collections:
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- Name: Vision Transformer
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Paper:
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Title: 'An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale'
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URL: https://paperswithcode.com/paper/an-image-is-worth-16x16-words-transformers-1
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Models:
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- Name: vit_base_patch16_224
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In Collection: Vision Transformer
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Metadata:
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FLOPs: 67394605056
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Parameters: 86570000
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File Size: 346292833
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Architecture:
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- Attention Dropout
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- Convolution
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- Dense Connections
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- Dropout
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- GELU
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- Layer Normalization
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- Multi-Head Attention
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- Scaled Dot-Product Attention
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- Tanh Activation
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Tasks:
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- Image Classification
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Training Techniques:
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- Cosine Annealing
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- Gradient Clipping
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- SGD with Momentum
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Training Data:
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- ImageNet
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- JFT-300M
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Training Resources: TPUv3
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ID: vit_base_patch16_224
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LR: 0.0008
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Epochs: 90
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Dropout: 0.0
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Crop Pct: '0.9'
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Batch Size: 4096
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Image Size: '224'
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Warmup Steps: 10000
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Weight Decay: 0.03
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/5f9aff395c224492e9e44248b15f44b5cc095d9c/timm/models/vision_transformer.py#L503
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 81.78%
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Top 5 Accuracy: 96.13%
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- Name: vit_base_patch16_384
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In Collection: Vision Transformer
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Metadata:
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FLOPs: 49348245504
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Parameters: 86860000
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File Size: 347460194
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Architecture:
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- Attention Dropout
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- Convolution
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- Dense Connections
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- Dropout
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- GELU
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- Layer Normalization
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- Multi-Head Attention
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- Scaled Dot-Product Attention
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- Tanh Activation
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Tasks:
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- Image Classification
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Training Techniques:
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- Cosine Annealing
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- Gradient Clipping
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- SGD with Momentum
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Training Data:
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- ImageNet
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- JFT-300M
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Training Resources: TPUv3
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ID: vit_base_patch16_384
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Crop Pct: '1.0'
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Momentum: 0.9
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Batch Size: 512
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Image Size: '384'
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Weight Decay: 0.0
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/5f9aff395c224492e9e44248b15f44b5cc095d9c/timm/models/vision_transformer.py#L522
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_384-83fb41ba.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 84.2%
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Top 5 Accuracy: 97.22%
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- Name: vit_base_patch32_384
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In Collection: Vision Transformer
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Metadata:
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FLOPs: 12656142336
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Parameters: 88300000
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File Size: 353210979
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Architecture:
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- Attention Dropout
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- Convolution
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- Dense Connections
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- Dropout
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- GELU
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- Layer Normalization
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- Multi-Head Attention
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- Scaled Dot-Product Attention
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- Tanh Activation
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Tasks:
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- Image Classification
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Training Techniques:
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- Cosine Annealing
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- Gradient Clipping
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- SGD with Momentum
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Training Data:
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- ImageNet
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- JFT-300M
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Training Resources: TPUv3
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ID: vit_base_patch32_384
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Crop Pct: '1.0'
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Momentum: 0.9
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Batch Size: 512
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Image Size: '384'
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Weight Decay: 0.0
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/5f9aff395c224492e9e44248b15f44b5cc095d9c/timm/models/vision_transformer.py#L532
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p32_384-830016f5.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 81.66%
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Top 5 Accuracy: 96.13%
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- Name: vit_base_resnet50_384
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In Collection: Vision Transformer
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Metadata:
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FLOPs: 49461491712
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Parameters: 98950000
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File Size: 395854632
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Architecture:
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- Attention Dropout
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- Convolution
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- Dense Connections
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- Dropout
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- GELU
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- Layer Normalization
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- Multi-Head Attention
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- Scaled Dot-Product Attention
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- Tanh Activation
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Tasks:
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- Image Classification
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Training Techniques:
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- Cosine Annealing
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- Gradient Clipping
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- SGD with Momentum
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Training Data:
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- ImageNet
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- JFT-300M
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Training Resources: TPUv3
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ID: vit_base_resnet50_384
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Crop Pct: '1.0'
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Momentum: 0.9
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Batch Size: 512
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Image Size: '384'
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Weight Decay: 0.0
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/5f9aff395c224492e9e44248b15f44b5cc095d9c/timm/models/vision_transformer.py#L653
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_384-9fd3c705.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 84.99%
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Top 5 Accuracy: 97.3%
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- Name: vit_large_patch16_224
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In Collection: Vision Transformer
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Metadata:
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FLOPs: 119294746624
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Parameters: 304330000
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File Size: 1217350532
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Architecture:
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- Attention Dropout
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- Convolution
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- Dense Connections
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- Dropout
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- GELU
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- Layer Normalization
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- Multi-Head Attention
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- Scaled Dot-Product Attention
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- Tanh Activation
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Tasks:
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- Image Classification
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Training Techniques:
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- Cosine Annealing
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- Gradient Clipping
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- SGD with Momentum
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Training Data:
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- ImageNet
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- JFT-300M
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Training Resources: TPUv3
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ID: vit_large_patch16_224
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Crop Pct: '0.9'
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Momentum: 0.9
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Batch Size: 512
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Image Size: '224'
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Weight Decay: 0.0
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/5f9aff395c224492e9e44248b15f44b5cc095d9c/timm/models/vision_transformer.py#L542
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_224-4ee7a4dc.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 83.06%
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Top 5 Accuracy: 96.44%
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- Name: vit_large_patch16_384
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In Collection: Vision Transformer
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Metadata:
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FLOPs: 174702764032
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Parameters: 304720000
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File Size: 1218907013
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Architecture:
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- Attention Dropout
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- Convolution
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- Dense Connections
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- Dropout
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- GELU
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- Layer Normalization
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- Multi-Head Attention
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- Scaled Dot-Product Attention
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- Tanh Activation
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Tasks:
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- Image Classification
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Training Techniques:
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- Cosine Annealing
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- Gradient Clipping
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- SGD with Momentum
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Training Data:
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- ImageNet
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- JFT-300M
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Training Resources: TPUv3
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ID: vit_large_patch16_384
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Crop Pct: '1.0'
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Momentum: 0.9
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Batch Size: 512
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Image Size: '384'
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Weight Decay: 0.0
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/5f9aff395c224492e9e44248b15f44b5cc095d9c/timm/models/vision_transformer.py#L561
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_384-b3be5167.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 85.17%
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Top 5 Accuracy: 97.36%
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- Name: vit_small_patch16_224
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In Collection: Vision Transformer
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Metadata:
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FLOPs: 28236450816
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Parameters: 48750000
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File Size: 195031454
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Architecture:
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- Attention Dropout
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- Convolution
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- Dense Connections
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- Dropout
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- GELU
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- Layer Normalization
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- Multi-Head Attention
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- Scaled Dot-Product Attention
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- Tanh Activation
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Tasks:
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- Image Classification
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Training Techniques:
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- Cosine Annealing
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- Gradient Clipping
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- SGD with Momentum
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Training Data:
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- ImageNet
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- JFT-300M
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Training Resources: TPUv3
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ID: vit_small_patch16_224
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Crop Pct: '0.9'
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Image Size: '224'
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/5f9aff395c224492e9e44248b15f44b5cc095d9c/timm/models/vision_transformer.py#L490
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/vit_small_p16_224-15ec54c9.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 77.85%
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Top 5 Accuracy: 93.42%
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-->
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