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* [Squash] Refator ViT (from #295) * Use base variable to simplify auto_aug setting * Use common PatchEmbed, remove HybridEmbed and refactor ViT init structure. * Add `output_cls_token` option and change the output format of ViT and input format of ViT head. * Update unit tests and add test for `output_cls_token`. * Support out_indices. * Standardize config files * Support resize position embedding. * Add readme file of vit * Rename config file * Improve docs about ViT. * Update docstring * Use local version `MultiheadAttention` instead of mmcv version. * Fix MultiheadAttention * Support `qk_scale` argument in `MultiheadAttention` * Improve docs and change `layer_cfg` to `layer_cfgs` and support sequence. * Use init_cfg to init Linear layer in VisionTransformerHead * update metafile * Update checkpoints and configs * Imporve docstring. * Update README * Revert GAP modification. |
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README.md
Introduction
English | 简体中文
MMClassification is an open source image classification toolbox based on PyTorch. It is a part of the OpenMMLab project.
Documentation: https://mmclassification.readthedocs.io/en/latest/
Major features
- Various backbones and pretrained models
- Bag of training tricks
- Large-scale training configs
- High efficiency and extensibility
License
This project is released under the Apache 2.0 license.
Changelog
v0.16.0 was released in 30/9/2021. Please refer to changelog.md for details and release history.
Benchmark and model zoo
Results and models are available in the model zoo.
Supported backbones:
- ResNet
- ResNeXt
- SE-ResNet
- SE-ResNeXt
- RegNet
- ShuffleNetV1
- ShuffleNetV2
- MobileNetV2
- MobileNetV3
- Swin-Transformer
Installation
Please refer to install.md for installation and dataset preparation.
Getting Started
Please see getting_started.md for the basic usage of MMClassification. There are also tutorials for finetuning models, adding new dataset, designing data pipeline, and adding new modules.
Citation
If you find this project useful in your research, please consider cite:
@misc{2020mmclassification,
title={OpenMMLab's Image Classification Toolbox and Benchmark},
author={MMClassification Contributors},
howpublished = {\url{https://github.com/open-mmlab/mmclassification}},
year={2020}
}
Contributing
We appreciate all contributions to improve MMClassification. Please refer to CONTRUBUTING.md for the contributing guideline.
Acknowledgement
MMClassification is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new classifiers.
Projects in OpenMMLab
- MMCV: OpenMMLab foundational library for computer vision.
- MIM: MIM Installs OpenMMLab Packages.
- MMClassification: OpenMMLab image classification toolbox and benchmark.
- MMDetection: OpenMMLab detection toolbox and benchmark.
- MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
- MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
- MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
- MMTracking: OpenMMLab video perception toolbox and benchmark.
- MMPose: OpenMMLab pose estimation toolbox and benchmark.
- MMEditing: OpenMMLab image and video editing toolbox.
- MMOCR: OpenMMLab toolbox for text detection, recognition and understanding.
- MMGeneration: OpenMMlab toolkit for generative models.