yangxue0827/RotationDetection
This is a tensorflow-based rotation detection benchmark, also called AlphaRotate. 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
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
- readme: https://github.com/yangxue0827/RotationDetection · fetched 2026-08-28 · 2bb520203acf
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| yangxue0827/RotationDetection | main | 23 |
For agents
markdown · JSON · MCP: product_card(name="yangxue0827/RotationDetection")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem