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https://github.com/amazon-science/gluonmm

A library of transformer models for computer vision and multi-modality research

https://github.com/amazon-science/gluonmm

Science Score: 36.0%

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    Links to: arxiv.org
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Keywords

computer-vision iccv-2021 multimodality pytorch transformer video
Last synced: 5 months ago · JSON representation

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A library of transformer models for computer vision and multi-modality research

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computer-vision iccv-2021 multimodality pytorch transformer video
Created over 4 years ago · Last pushed over 4 years ago
Metadata Files
Readme Contributing License Code of conduct

README.md

GluonMM

GluonMM is a library of transformer models for computer vision and multi-modality research. It contains reference implementations of widely adopted baseline models and also research work from Amazon Research.

Install

First, clone the repository locally, git clone https://github.com/amazon-research/gluonmm.git

Then install dependencies, ``` conda create -n gluonmm python=3.7 conda activate gluonmm conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch pip install timm tensorboardX yacs tqdm requests pandas decord scikit-image opencv-python

Install apex for half-precision training (optional)

git clone https://github.com/NVIDIA/apex cd apex pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cppext" --global-option="--cudaext" ./ ```

We have extensively tested the usage with PyTorch 1.8.1 and torchvision 0.9.1 with CUDA 10.2.

Model zoo

Image classification

Video action recognition

Usage

For detailed usage, please refer to the README file in each model family. For example, the training, evaluation and model zoo information of video transformer VidTr can be found at here.

Security

See CONTRIBUTING for more information.

License

This project is licensed under the Apache-2.0 License.

Acknowledgement

Parts of the code are heavily derived from pytorch-image-models, DeiT, Swin-transformer, vit-pytorch and vision_transformer.

Owner

  • Name: Amazon Science
  • Login: amazon-science
  • Kind: organization

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