Clarifai/mmclassification

Clarifai/mmclassification

Description: OpenMMLab Image Classification Toolbox and Benchmark

Language: Python

License: Apache-2.0

Stars: 0

Forks: 0

Open issues: 0

Created: 2022-04-25T21:11:23Z

Pushed: 2024-10-08T00:03:51Z

Default branch: master

Fork: yes

Parent repository: open-mmlab/mmpretrain

Archived: no

README:

   

📘 Documentation | 🛠️ Installation | 👀 Model Zoo | 🆕 Update News | 🤔 Reporting Issues

Introduction

English | [简体中文](/README_zh-CN.md)

MMClassification is an open source image classification toolbox based on PyTorch. It is a part of the OpenMMLab project.

The master branch works with PyTorch 1.5+.

Major features

• Various backbones and pretrained models
• Bag of training tricks
• Large-scale training configs
• High efficiency and extensibility
• Powerful toolkits

What's new

v0.22.0 was released in 30/3/2022.

Highlights of the new version:

• Support a series of CSP Network, such as CSP-ResNet, CSP-ResNeXt and CSP-DarkNet.
• A new CustomDataset class to help you build dataset of yourself!
• Support new backbones - ConvMixer, RepMLP and new dataset - CUB dataset.

v0.21.0 was released in 4/3/2022.

Highlights of the new version:

• Support ResNetV1c and Wide-ResNet, and provide pre-trained models.
• Support dynamic input shape for ViT-based algorithms. Now our ViT, DeiT, Swin-Transformer and T2T-ViT support forwarding with any input shape.
• Reproduce training results of DeiT. And our DeiT-T and DeiT-S have higher accuracy comparing with the official weights.

Please refer to [changelog.md](docs/en/changelog.md) for more details and other release history.

Installation

Below are quick steps for installation:

conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y
conda activate open-mmlab
pip3 install openmim
mim install mmcv-full
git clone https://github.com/open-mmlab/mmclassification.git
cd mmclassification
pip3 install -e .

Please refer to install.md for more detailed installation and dataset preparation.

Getting Started

Please see Getting Started for the basic usage of MMClassification. There are also tutorials:

• Learn about Configs
• Fine-tune Models
• Add New Dataset
• Customizie Data Pipeline
• Add New Modules
• Customizie Schedule
• Customizie Runtime Settings

Colab tutorials are also provided:

• Learn about MMClassification Python API: Preview the notebook or directly run on Colab.
• Learn about MMClassification CLI tools: Preview the notebook or directly run on Colab.

Model zoo

Results and models are available in the model zoo.

Supported backbones

• [x] VGG
• [x] ResNet
• [x] ResNeXt
• [x] SE-ResNet
• [x] SE-ResNeXt
• [x] RegNet
• [x] ShuffleNetV1
• [x] ShuffleNetV2
• [x] MobileNetV2
• [x] MobileNetV3
• [x] Swin-Transformer
• [x] RepVGG
• [x] Vision-Transformer
• [x] Transformer-in-Transformer
• [x] Res2Net
• [x] MLP-Mixer
• [x]…