kornia/kornia

Health v2 · maintenance only

Adoption not part of the score

11327 stars · 1236 forks observed · 2026-08-28

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

Kornia is a differentiable computer vision library built on PyTorch, offering GPU-accelerated image processing, augmentations, and geometric vision algorithms. It includes pre-trained models for tasks like detection, matching, and segmentation, and integrates into deep learning pipelines with auto-differentiation support.

Use cases

• augment images for training deep learning models
• estimate homographies and camera calibration
• detect edges and apply differentiable filters on GPU
• match image features and stitch panoramas
• run pre-trained vision models like SAM or LoFTR
• compute differentiable geometry losses for training

When to choose

• you need differentiable image operations inside PyTorch training loops
• you want GPU-accelerated batch image augmentation
• you work on geometric vision like SLAM, calibration, or 3D reconstruction
• you need pre-trained vision models with a PyTorch-native API

When to avoid

• you need a lightweight CPU-only image processing tool like OpenCV for simple scripts
• you don't use PyTorch and can't take its dependency
• you need production image processing pipelines without deep learning

Facets

library · maturity active

image-processing computer-vision machine-learning deep-learning computer-vision image-processing deep-learning machine-learning robotics python cross-platform pytorch differentiable-programming data-augmentation geometric-vision camera-calibration image-matching spatial-ai gpu

Member repositories

For agents

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