Metadata-Version: 2.1
Name: vision_transformer_pytorch
Version: 1.0.2
Summary: VisionTransformer implemented in PyTorch.
Home-page: https://github.com/tczhangzhi/VisionTransformer-PyTorch
Author: ZHANG Zhi
Author-email: zhangzhi2018@email.szu.edu.cn
License: Apache
Description: 
        # Vision Transformer Pytorch
        This project is modified from [lukemelas](https://github.com/lukemelas)/[EfficientNet-PyTorch](https://github.com/lukemelas/EfficientNet-PyTorch) and [asyml](https://github.com/asyml)/[vision-transformer-pytorch](https://github.com/asyml/vision-transformer-pytorch) to provide out-of-box API for you to utilize VisionTransformer as easy as EfficientNet.
        
        ### Quickstart
        
        Install with `pip install vision_transformer_pytorch` and load a pretrained VisionTransformer with:
        
        ```
        from vision_transformer_pytorch import VisionTransformer
        model = VisionTransformer.from_pretrained('ViT-B_16')
        ```
        
        ### About Vision Transformer PyTorch
        
        Vision Transformer Pytorch is a PyTorch re-implementation of Vision Transformer based on one of the best practice of commonly utilized deep learning libraries, [EfficientNet-PyTorch](https://github.com/lukemelas/EfficientNet-PyTorch), and an elegant implement of VisionTransformer, [vision-transformer-pytorch](https://github.com/asyml/vision-transformer-pytorch). In this project, we aim to make our PyTorch implementation as simple, flexible, and extensible as possible.
        
        If you have any feature requests or questions, feel free to leave them as GitHub issues!
        
        ### Installation
        
        Install via pip:
        
        ```
        pip install vision_transformer_pytorch
        ```
        
        Or install from source:
        
        ```
        git clone https://github.com/tczhangzhi/VisionTransformer-Pytorch
        cd VisionTransformer-Pytorch
        pip install -e .
        ```
        
        ### Usage
        
        #### Loading pretrained models
        
        Load an EfficientNet:
        
        ```
        from vision_transformer_pytorch import VisionTransformer
        model = VisionTransformer.from_name('ViT-B_16')
        ```
        
        Load a pretrained EfficientNet:
        
        ```
        from vision_transformer_pytorch import VisionTransformer
        model = VisionTransformer.from_pretrained('ViT-B_16')
        # inputs = torch.randn(1, 3, *model.image_size)
        # model(inputs)
        # model.extract_features(inputs)
        ```
        
        Default hyper parameters:
        
        | Param\Model       | ViT-B_16 | ViT-B_32 | ViT-L_16 | ViT-L_32 |
        | ----------------- | -------- | -------- | -------- | -------- |
        | image_size        | 384      | 384      | 384      | 384      |
        | patch_size        | 16       | 32       | 16       | 32       |
        | emb_dim           | 768      | 768      | 1024     | 1024     |
        | mlp_dim           | 3072     | 3072     | 4096     | 4096     |
        | num_heads         | 12       | 12       | 16       | 16       |
        | num_layers        | 12       | 12       | 24       | 24       |
        | num_classes       | 1000     | 1000     | 1000     | 1000     |
        | attn_dropout_rate | 0.0      | 0.0      | 0.0      | 0.0      |
        | dropout_rate      | 0.1      | 0.1      | 0.1      | 0.1      |
        
        If you need to modify these hyper parameters, please use:
        
        ```
        from vision_transformer_pytorch import VisionTransformer
        model = VisionTransformer.from_name('ViT-B_16', image_size=256, patch_size=64, ...)
        ```
        
        #### ImageNet
        
        See `examples/imagenet` for details about evaluating on ImageNet.
        
        ### Contributing
        
        If you find a bug, create a GitHub issue, or even better, submit a pull request. Similarly, if you have questions, simply post them as GitHub issues.
        
        I look forward to seeing what the community does with these models!
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Requires-Python: >=3.5.0
Description-Content-Type: text/markdown
