sfzhang15/ATSS

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1086 stars · 161 forks observed · 2026-08-28

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

Official PyTorch implementation of ATSS (Adaptive Training Sample Selection), a CVPR 2020 Oral paper on object detection. It automatically selects positive and negative training samples based on statistical characteristics of objects, bridging the gap between anchor-based and anchor-free detectors and improving state-of-the-art detectors to 50.7% AP on MS COCO without overhead.

Use cases

• train an object detector with adaptive positive/negative sample selection
• reproduce CVPR 2020 ATSS results on MS COCO
• improve anchor-free detectors like FCOS with center sampling
• run inference with a pretrained ATSS ResNet-50 FPN model
• compare anchor-based vs anchor-free detection training strategies
• use ATSS as a baseline for object detection research

When to choose

• you need a research-grade object detector with adaptive training sample selection
• you want to reproduce or build on the ATSS CVPR 2020 paper
• you are benchmarking anchor-based vs anchor-free detectors on COCO

When to avoid

• you need a production-ready, actively maintained detection framework
• you want plug-and-play detection without building maskrcnn-benchmark dependencies
• you need detection on platforms other than Linux with GPUs

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision image-processing computer-vision deep-learning machine-learning python object-detection anchor-free-detection cvpr-2020 pytorch research-code coco training-sample-selection linux gpu

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markdown · JSON · MCP: product_card(name="sfzhang15/ATSS")