bc1b08ba41
* add vgg * add vgg model coversion tool * fix out_indices and docstr * add vgg models in configs * add params, flops and accuracy in docs * add pretrained models url * use ConvModule and refine var names * update vgg conversion tool * modify bn config * add docs for arch_setting * add unit test for vgg * rm debug code * update vgg pretrained models
Introduction
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.
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.
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.