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README.md

Timm Example

PyTorch Image Models

Abstract

PyTorch Image Models (timm) is a collection of image models, layers, utilities, optimizers, schedulers, data-loaders / augmentations, and reference training / validation scripts that aim to pull together a wide variety of SOTA models with ability to reproduce ImageNet training results.

Results and Models

RetinaNet

Backbone Style Lr schd Mem (GB) Inf time (fps) box AP Config Download
R-50 pytorch 1x config
EfficientNet-B1 - 1x config

Usage

Install additional requirements

MMDetection supports timm backbones via TIMMBackbone, a wrapper class in MMClassification. Thus, you need to install mmcls in addition to timm. If you have already installed requirements for mmdet, run

pip install 'dataclasses; python_version<"3.7"'
pip install timm
pip install 'mmcls>=0.20.0'

See this document for the details of MMClassification installation.

Edit config

  • See example configs for basic usage.
  • See the documents of timm feature extraction and TIMMBackbone for details.
  • Which feature map is output depends on the backbone. Please check backbone out_channels and backbone out_strides in your log, and modify model.neck.in_channels and model.backbone.out_indices if necessary.
  • If you use Vision Transformer models that do not support features_only=True, add custom_hooks = [] to your config to disable NumClassCheckHook.

Citation

@misc{rw2019timm,
  author = {Ross Wightman},
  title = {PyTorch Image Models},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  doi = {10.5281/zenodo.4414861},
  howpublished = {\url{https://github.com/rwightman/pytorch-image-models}}
}