# WZMIAOMIAO/deep-learning-for-image-processing

deep learning for image processing including classification and object-detection etc.

Repository: https://github.com/WZMIAOMIAO/deep-learning-for-image-processing
Canonical: https://ross.abutalabs.com/products/deep-learning-for-image-processing
Language: Python
License: GPL-3.0
License Family: copyleft
Topics: pytorch, tensorflow2, classification, object-detection, bilibili, deep-learning, segmentation
Last push: 2026-01-01T07:16:34+00:00

## Health v2 (maintenance only)
Score: 59/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 60, release rhythm 35, longevity 100
- inputs: {"age_days": 2484, "days_push": 244, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 26359, forks 8176 (observed 2026-08-28T04:11:46.890660+00:00)

## What it is
A Chinese-language educational repository of deep learning tutorials for image processing, covering classification, object detection, and segmentation networks. Each architecture (LeNet through Vision Transformer) is implemented and trained in both PyTorch and TensorFlow2, accompanied by Bilibili video lectures and PPT slides.

## Use cases
- learn deep learning for image classification from scratch
- understand how ResNet or Vision Transformer works with code
- implement object detection networks in PyTorch and TensorFlow
- study classic CNN architectures like VGG and GoogLeNet
- find tutorial code for semantic segmentation models
- follow a video course on deep learning for computer vision

## When to choose
- you want guided tutorials with video explanations alongside code
- you want to see the same network implemented in both PyTorch and TensorFlow2
- you are a student or beginner learning image classification, detection, or segmentation

## When to avoid
- you need production-ready, maintained model implementations
- you need English-language documentation
- you need a packaged library with pip installation and stable APIs

## Facets
- artifact type: learning-resource
- maturity: active
- function: deep-learning, machine-learning, image-processing, computer-vision
- domain: deep-learning, computer-vision, image-processing, tutorials
- platform: python
- tags: pytorch, tensorflow2, image-classification, object-detection, semantic-segmentation, bilibili-videos, educational, vision-transformer

## Member repositories
- WZMIAOMIAO/deep-learning-for-image-processing (main) score 59

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:46.890660+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 2026-08-29T16:55:53.015314+00:00, confidence not recorded.
  - readme: https://github.com/WZMIAOMIAO/deep-learning-for-image-processing (fetched 2026-08-28T04:11:46.890660+00:00, sha 20a75d2d94ad)
- Data as of 2026-08-30T08:39:29.467469+00:00.