vision-transformer-pytorch 1.0.2

VisionTransformer implemented in PyTorch.

Vision Transformer Pytorch

This project is modified from lukemelas/EfficientNet-PyTorch and 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, and an elegant implement of VisionTransformer, 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!