mirrors
/
mmclassification
mirror of https://github.com/open-mmlab/mmclassification.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity
OpenMMLab Image Classification Toolbox and Benchmark
170 Commits
8 Branches
41 Tags
36 MiB
Python 99.7%
Shell 0.2%
Dockerfile 0.1%
 
 
 
Go to file
LXXXXR 149ee3a30d
support img input as ndarray in inference.py (#87) 
* support img input as ndarray

* revise according to comments
 		2020-11-19 18:58:25 +08:00
.github Do not install mmcv through requirements (#90) 2020-11-19 00:00:12 +08:00
configs Use build_runner (#54) 2020-10-15 21:12:50 +08:00
demo Visualize results on image demo (#58) 2020-10-10 16:33:27 +08:00
docs Use build_runner (#54) 2020-10-15 21:12:50 +08:00
mmcls support img input as ndarray in inference.py (#87) 2020-11-19 18:58:25 +08:00
requirements Do not install mmcv through requirements (#90) 2020-11-19 00:00:12 +08:00
resources
…
tests add get_class in base_dataset (#85) 2020-11-12 14:22:02 +08:00
tools Add pytorch2onnx (#20) 2020-09-30 20:13:36 +08:00
.gitignore bump version to 0.6.0 (#62) 2020-10-11 00:12:04 +08:00
.pre-commit-config.yaml
…
.readthedocs.yml
…
LICENSE
…
README.md rename detectors to classifiers (#19) 2020-08-05 00:43:13 +08:00
requirements.txt workflow: modify build, add deploy (#61) 2020-10-10 22:15:50 +08:00
setup.cfg workflow: modify build, add deploy (#61) 2020-10-10 22:15:50 +08:00
setup.py fix pypi release error (#64) 2020-10-11 00:26:34 +08:00

README.md

Build Status Documentation Status codecov license

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/

demo

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.