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* implement the conformer * format code style * format code style * reuse the TransformerEncoderLayer in the vision_transformer.py * Modify variable name * delete unused params * Remove warning info in Conformer head since it already exists in Conformer. * Rename some variables * Add unit tests * Use `getattr` instead of `get_submodule`. * Remove some useless layers * Refactor conformer and add configs * Update configs and add metafile. * Fix unit tests * Update README Co-authored-by: mzr1996 <mzr1996@163.com> |
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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
v0.18.0 was released in 30/11/2021.
Highlights of the new version:
- Support MLP-Mixer backbone and provide pre-trained checkpoints.
- Add a tool to visualize the learning rate curve of the training phase. Welcome to use with the tutorial!
v0.17.0 was released in 29/10/2021.
Highlights of this version:
- Support Tokens-to-Token ViT backbone and Res2Net backbone. Welcome to use!
- Support ImageNet21k dataset.
- Add a pipeline visualization tool. Try it with the tutorials!
Please refer to changelog.md for more details and other release history.
Benchmark and model zoo
Results and models are available in the model zoo.
Supported backbones:
- VGG
- ResNet
- ResNeXt
- SE-ResNet
- SE-ResNeXt
- RegNet
- ShuffleNetV1
- ShuffleNetV2
- MobileNetV2
- MobileNetV3
- Swin-Transformer
- RepVGG
- Vision-Transformer
- Transformer-in-Transformer
- Res2Net
- MLP-Mixer
- DeiT
- Conformer
- EfficientNet
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:
- learn about configs
- finetuning models
- adding new dataset
- designing data pipeline
- adding new modules
- customizing schedule
- customizing runtime settings
Colab tutorials are also provided. To learn about MMClassification Python API, you may preview the notebook here or directly run on Colab. To learn about MMClassification shell tools, you may preview the notebook here or directly run on Colab.
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.
- MMFlow OpenMMLab optical flow toolbox and benchmark.
- MMFewShot: OpenMMLab FewShot Learning Toolbox and Benchmark.
- MMHuman3D: OpenMMLab 3D Human Parametric Model Toolbox and Benchmark.