Peterande/D-FINE

Health v2 · maintenance only

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3305 stars · 318 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

D-FINE is the official PyTorch implementation of an ICLR 2025 Spotlight paper that redefines the regression task in DETR-style detectors as fine-grained distribution refinement, achieving state-of-the-art real-time object detection on COCO. It provides pretrained models, training, and inference tooling for real-time object detection.

Use cases

• train a state-of-the-art real-time object detector on custom datasets
• run fast object detection on images or video
• fine-tune a DETR-based detection model
• deploy an efficient object detection model with low latency
• reproduce ICLR 2025 D-FINE paper results on COCO
• export detection models for edge or production inference

When to choose

• you need a fast, accurate real-time object detector with pretrained weights
• you want a modern DETR-family detector that is easy to train without hand-tuned anchors or NMS
• you need a research baseline for detection regression refinement

When to avoid

• you need segmentation, tracking, or other vision tasks beyond bounding-box detection
• you lack GPU resources for training DETR-style models
• you need a detector integrated into a larger framework like MMDetection or Ultralytics

Facets

library · maturity active

computer-vision machine-learning deep-learning computer-vision deep-learning artificial-intelligence python cross-platform object-detection detr real-time-detection iclr-2025 pytorch model-zoo gpu linux

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