torrvision/crfasrnn

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

1335 stars · 455 forks observed · 2026-08-28

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

Reference implementation of CRF-RNN, an ICCV 2015 semantic image segmentation method that integrates conditional random fields into a neural network, built on the Caffe deep learning library. It includes a trained model recognizing 20 object classes and links to PyTorch and Keras ports.

Use cases

• semantic image segmentation of objects in photos
• run CRF-RNN segmentation model on my own images
• integrate CRF layers into a CNN for pixel-wise labeling
• reproduce ICCV 2015 CRF as RNN paper results
• segment 20 PASCAL VOC object classes in images

When to choose

• you need the original CRF-RNN implementation or its trained Caffe model
• you are reproducing or extending the ICCV 2015 paper
• you want to study how CRFs are embedded in deep networks

When to avoid

• you want a modern maintained framework - use the PyTorch or Keras ports instead
• you need segmentation of classes beyond the 20 trained ones without retraining
• your stack does not support the legacy Caffe/MATLAB toolchain

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision image-processing computer-vision deep-learning image-processing machine-learning cpp python semantic-segmentation conditional-random-fields caffe crf-rnn research-code matlab linux gpu

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