65410b05ad
* Refactor Mobilenetv3 structure and add ConvClsHead. * Change model's name from 'MobileNetv3' to 'MobileNetV3' * Modify configs for MobileNetV3 on CIFAR10. And add MobileNetV3 configs for imagenet * Fix activate setting bugs in MobileNetV3. And remove bias in SELayer. * Modify unittest * Remove useless config and file. * Fix mobilenetv3-large arch setting * Add dropout option in ConvClsHead * Fix MobilenetV3 structure according to torchvision version. 1. Remove with_expand_conv option in InvertedResidual, it should be decided by channels. 2. Revert activation function, should before SE layer. * Format code. * Rename MobilenetV3 arch "big" to "large". * Add mobilenetv3_small torchvision training recipe * Modify default `out_indices` of MobilenetV3, now it will change according to `arch` if not specified. * Add MobilenetV3 large config. * Add mobilenetv3 README * Modify InvertedResidual unit test. * Refactor ConvClsHead to StackedLinearClsHead, and add unit tests. * Add unit test for `simple_test` of `StackedLinearClsHead`. * Fix typo Co-authored-by: Yidi Shao <ydshao@smail.nju.edu.cn>
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
v0.12.0 was released in 3/6/2021. 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
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.
- 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.