# hoya012/deep_learning_object_detection

A paper list of object detection using deep learning.

Repository: https://github.com/hoya012/deep_learning_object_detection
Canonical: https://ross.abutalabs.com/products/deep_learning_object_detection
Language: Python
License Family: other
Topics: deep-learning, deep-neural-networks, deeplearning, objectdetection, object-detection
Last push: 2024-02-12T09:30:31+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2907, "days_push": 933, "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 11385, forks 2745 (observed 2026-08-28T04:10:46.889519+00:00)

## What it is
A curated list of research papers on object detection using deep learning, spanning 2014 to 2020, with performance tables, dataset papers, and links to official and unofficial code implementations. It is a reference resource rather than executable software.

## Use cases
- find papers on object detection with deep learning
- survey the history of object detection models from 2014 to 2020
- find code implementations for detection papers like YOLO and Faster R-CNN
- compare performance benchmarks of object detection models
- find datasets for training object detection models
- get reading recommendations for learning object detection

## When to choose
- you need a curated reading list of object detection research papers
- you want historical context and evolution of detection architectures
- you want links to official and unofficial code for detection papers

## When to avoid
- you need a ready-to-use object detection model or library
- you need up-to-date coverage of recent research after 2020
- you need a maintained tool with a license and active development

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: deep-learning, computer-vision, documentation
- domain: deep-learning, computer-vision, tutorials, awesome-lists
- platform: cross-platform
- tags: awesome-list, paper-list, object-detection, survey, research-papers

## Member repositories
- hoya012/deep_learning_object_detection (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:46.889519+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 2026-08-29T17:15:41.140148+00:00, confidence not recorded.
  - readme: https://github.com/hoya012/deep_learning_object_detection (fetched 2026-08-28T04:10:46.889519+00:00, sha 997ea6a47555)
- Data as of 2026-08-30T08:39:29.467469+00:00.