Fine-grained-Visual-Analysis-Library

Introduction

FGVCLib is an open-source and well documented library for Fine-grained Visual Classification. It is based on Pytorch with performance and friendly API. Our code is pythonic, and the design is consistent with torchvision. You can easily develop new algorithms, or readily apply existing algorithms. The branch works with torch 1.12.1, torchvision 0.13.1.

For more details and the tutorials about the FGVCLib, see FGVCLib

Major features

• Modular Design

  We decompose the detection framework into different components and one can easily construct a customized object detection framework by combining different modules.

• State of the art We implement state-of-the-art methods by the FGVCLib, PMG, PMG_V2, MCL, API-Net, CAL, TransFG, PIM.

Installation

Please refer to Installation for installation instructions.

Getting started

Please see get_started.md for the basic usage of FGVCLib. We provide the tutorials for:

• with existing data existing model
• with existing data new model
• learn about apis
• learn about configs
• learn about criterions
• learn about datasets
• learn about metrics
• learn about model
• learn about transforms
• learn about the tools

Overview of Benchmark and Model Zoo

Architectures

Fine-grained Visual Classification	Other
Baseline_ResNet50 Mutual-Channel-Loss PMG-ResNet50 PMG_V2_ResNet50 API-Net CAL TransFG PIM	visualization

Components

Backbones	Encoders	Heads	Necks	Sotas
Resnet VGG	Global Max Pooling Global Avg Pooling Max Pooling 2d	Classifier_1_FC Classifier_2_FC	Multi-scale Convolution neck	Baseline_ResNet50 Mutual-Channel-Loss PMG-ResNet50 PMG_V2_ResNet50 API-Net CAL TransFG PIM

Contact

Thanks for your attention! If you have any suggestion or question, you can leave a message here or contact us directly:

• changdongliang@bupt.edu.cn
• mazhanyu@bupt.edu.cn

Others

Based on the fgvclib, we have developed an FGVC WeChat applet for fine-grained visual classification in practice, which can be accessed by searching "细粒度图像分类" in WeChat, and there is a demo: https://reurl.cc/rRZE7O.

Citation

If you find this library useful in your research, please consider citing:

@misc{Chang2023,
  author = {Dongliang, Chang and Ruoyi, Du and Xinran, Wang and Yuqi, Yang and Yi-Zhe, Song and Zhanyu, Ma},
  title = {Fine-grained Visual Analysis Library},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/PRIS-CV/Fine-grained-Visual-Analysis-Library}}
}