princeton-vl/CornerNet-Lite

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1771 stars · 413 forks observed · 2026-08-28

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

CornerNet-Lite is the official PyTorch implementation of the paper 'CornerNet-Lite: Efficient Keypoint Based Object Detection', providing training, evaluation, and testing code for keypoint-based object detectors. It includes three pretrained detectors (CornerNet-Saccade, CornerNet-Squeeze, and CornerNet) that can be applied to images via a simple Python API.

Use cases

• run object detection on images with pretrained models
• reproduce results from the CornerNet-Lite paper
• train a keypoint-based object detector
• evaluate object detection accuracy on benchmarks
• use a fast lightweight detector for real-time inference
• detect objects in photos and draw bounding boxes

When to choose

• you need efficient keypoint-based object detection with pretrained models
• you want to reproduce or build on the CornerNet-Lite research
• you need a lightweight detector like CornerNet-Squeeze for faster inference

When to avoid

• you need a maintained production-ready detection framework with broad model support
• you require Windows or non-CUDA environments
• you want the latest detection architectures or active community support

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision image-processing computer-vision deep-learning machine-learning python object-detection keypoint-detection pytorch cornernet research-code efficient-inference linux gpu

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