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yangxue0827/RotationDetection

This is a tensorflow-based rotation detection benchmark, also called AlphaRotate. observed · 2026-08-28

github.com/yangxue0827/RotationDetection · homepage · Python · Apache-2.0 (permissive) observed · 2026-08-28

Health v2 · maintenance only

23/100

  • Activity 0
  • Release rhythm 8
  • Longevity 100
How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.

  • gap_med: n/a
  • age_days: 2137
  • days_rel: n/a
  • days_push: 664
  • n_releases_24m: 0

Full methodology

Adoption not part of the score

1118 stars · 179 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

AlphaRotate is a TensorFlow-based benchmark and toolbox for rotated (oriented) object detection, implementing detectors such as R2CNN, RetinaNet-based rotation variants, and related techniques with support for standard backbones and FPN-style necks. It ships with training and evaluation pipelines for widely used rotated detection datasets including DOTA, HRSC2016, ICDAR2015/2017, MSRA-TD500, UCAS-AOD, and Total-Text, and is maintained as an academic research benchmark by Shanghai Jiao Tong University.

Use cases

  • detect objects with rotated bounding boxes in aerial or remote sensing images
  • train a rotated object detector on the DOTA dataset
  • benchmark oriented object detection algorithms like R2CNN
  • perform scene text detection with arbitrary orientations on ICDAR datasets
  • compare rotated detection models under a single TensorFlow framework
  • reproduce research results for oriented bounding box detection

When to choose

  • you work with TensorFlow and need rotated/oriented object detection
  • your research targets remote sensing, aerial imagery, or rotated scene text
  • you want a unified benchmark to compare multiple rotation detection algorithms
  • you need pretrained or reproducible baselines on DOTA, HRSC2016, or ICDAR benchmarks

When to avoid

  • your stack is PyTorch-first (consider MMRotate or MMDetection-based alternatives)
  • you only need standard horizontal bounding box detection
  • you need a production-ready, commercially supported detection service rather than a research benchmark
  • you require lightweight inference on edge devices with minimal dependencies

Facets

framework · maturity active

machine-learning deep-learning computer-vision image-processing computer-vision deep-learning machine-learning artificial-intelligence image-processing python cross-platform object-detection rotated-bounding-box oriented-object-detection remote-sensing aerial-imagery text-detection benchmark dota-dataset tensorflow alpharotate research gpu linux

1 source

Member repositories

RepositoryRoleHealth v2
yangxue0827/RotationDetectionmain23

For agents

markdown · JSON · MCP: product_card(name="yangxue0827/RotationDetection")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem