# Fafa-DL/Awesome-Backbones

Integrate deep learning models for image classification | Backbone learning/comparison/magic modification project

Repository: https://github.com/Fafa-DL/Awesome-Backbones
Canonical: https://ross.abutalabs.com/products/awesome-backbones
Language: Python
License Family: other
Topics: pytorch, image-classification, transformer, cnn, pytorch-classification, deep-learning, resnet, swin-transformer
Last push: 2025-01-17T02:27:19+00:00

## Health v2 (maintenance only)
Score: 33/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 1, release rhythm 35, longevity 100
- inputs: {"age_days": 1702, "days_push": 594, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1953, forks 270 (observed 2026-08-28T04:05:58.435145+00:00)

## What it is
A PyTorch-based framework that integrates many deep learning backbone models (CNNs and vision transformers like ResNet, EfficientNet, Swin Transformer) for image classification. It is designed for learning, comparing, and easily modifying backbone architectures with config-driven training and evaluation pipelines.

## Use cases
- train an image classifier with a pretrained backbone
- compare CNN vs transformer backbones on my dataset
- fine-tune resnet or swin transformer on custom images
- export a classification model to ONNX
- learn how vision transformer backbones are implemented
- generate class activation maps for a classifier
- train a classifier on a small dataset with configurable augmentation

## When to choose
- you want a lightweight, config-driven PyTorch toolbox focused on classification backbones
- you want to experiment with or modify many backbone architectures in one codebase
- you are learning deep learning and want readable model implementations with tutorials

## When to avoid
- you need detection, segmentation, or other vision tasks beyond classification
- you need a production-grade framework with long-term commercial support
- you require a permissively licensed project - it currently has no license file

## Facets
- artifact type: framework
- maturity: active
- function: deep-learning, machine-learning, image-processing
- domain: deep-learning, computer-vision, image-processing, machine-learning
- platform: python, cross-platform
- tags: pytorch, image-classification, backbones, cnn, vision-transformer, resnet, swin-transformer, model-zoo, onnx-export, gpu

## Member repositories
- Fafa-DL/Awesome-Backbones (main) score 33

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:58.435145+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:06:33.263890+00:00, confidence not recorded.
  - readme: https://github.com/Fafa-DL/Awesome-Backbones (fetched 2026-08-28T04:05:58.435145+00:00, sha 90085f46569b)
- Data as of 2026-08-30T08:39:29.467469+00:00.