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* add imagenet bs 4096 * add vit_base_patch16_224_finetune * add vit_base_patch16_224_pretrain * add vit_base_patch16_384_finetune * add vit_base_patch16_384_finetune * add vit_b_p16_224_finetune_imagenet * add vit_b_p16_224_pretrain_imagenet * add vit_b_p16_384_finetune_imagenet * add vit * add vit * add vit head * vit unitest * keep up with ClsHead * test vit * add flag to determiine whether to calculate acc during training * Changes related to mmcv1.3.0 * change checkpoint saving interval to 10 * add label smooth * default_runtime.py recovery * docformatter * docformatter * delete 2 lines of comments * delete configs/_base_/schedules/imagenet_bs4096.py * add configs/_base_/schedules/imagenet_bs2048_AdamW.py * rename imagenet_bs4096.py to imagenet_bs2048_AdamW.py * add AutoAugment * fix weight decay in vit * change eval interval to 10 * add mytrain.py for test * test before layers * test attr in layers * test classifier * delete mytrain.py * delete @torch.jit.ignore * change eval interval back to 1 * add some comments to imagenet_bs2048_AdamW * add some comments |
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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
v0.10.0 was released in 1/4/2021. 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.