# pprp/SimpleCVReproduction

Replication of simple CV Projects including attention, classification, detection, keypoint detection, etc.

Repository: https://github.com/pprp/SimpleCVReproduction
Canonical: https://ross.abutalabs.com/products/simplecvreproduction
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: computer-vision, paper-reproduction, demo, cv, pytorch, classification, object-detection, face-detection, landmark, attention
Last push: 2025-08-14T06:55:55+00:00

## Health v2 (maintenance only)
Score: 48/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 36, release rhythm 35, longevity 100
- inputs: {"age_days": 2457, "days_push": 384, "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 1261, forks 318 (observed 2026-08-28T04:04:10.081978+00:00)

## What it is
A curated collection of simplified, well-commented reproductions of computer vision models including attention mechanisms, classification, object detection, and keypoint detection, built with PyTorch. It is aimed at beginners who want readable code, notes, and simple implementations of CV papers.

## Use cases
- learn how attention modules like CBAM and SENet work in computer vision
- reproduce simple object detection models like CenterNet
- study annotated PyTorch implementations of CV papers
- find plug-and-play modules to add to YOLOv3
- get started with computer vision as a beginner
- understand Vision Transformer variants with readable code
- augment small-object datasets for detection

## When to choose
- you are a beginner learning computer vision through readable code
- you want simplified, commented reproductions of CV papers
- you need plug-and-play attention modules for detection models

## When to avoid
- you need production-ready, well-maintained CV libraries
- you require state-of-the-art performance or full official implementations
- you need commercial-use-licensed content

## Facets
- artifact type: learning-resource
- maturity: active
- function: computer-vision, image-processing, machine-learning
- domain: computer-vision, deep-learning, tutorials
- platform: python
- tags: pytorch, paper-reproduction, attention-mechanism, object-detection, classification, keypoint-detection, beginner-friendly, jupyter-notebook

## Member repositories
- pprp/SimpleCVReproduction (main) score 48

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:10.081978+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 2026-08-30T05:04:20.091875+00:00, confidence not recorded.
  - readme: https://github.com/pprp/SimpleCVReproduction (fetched 2026-08-28T04:04:10.081978+00:00, sha fa47ffac03ce)
- Data as of 2026-08-30T08:39:29.467469+00:00.