shelhamer/fcn.berkeleyvision.org

Health v2 · maintenance only

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Adoption not part of the score

3350 stars · 1429 forks observed · 2026-08-28

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

Reference implementation of Fully Convolutional Networks (FCN) for semantic segmentation from the CVPR 2015 / PAMI 2016 papers, built on Caffe. It includes pretrained PASCAL VOC models (FCN-32s/16s/8s and FCN-AlexNet) and training/evaluation code.

Use cases

• run semantic segmentation on images with pretrained FCN models
• reproduce FCN PASCAL VOC benchmark results
• fine-tune FCN-32s/16s/8s on my own segmentation dataset
• study the original fully convolutional network implementation
• train an FCN-AlexNet segmentation model in Caffe

When to choose

• you need the canonical FCN models and code from the original authors
• your stack is already Caffe-based
• you want pretrained PASCAL VOC segmentation weights

When to avoid

• you use PyTorch or TensorFlow rather than Caffe
• you need actively maintained or production-ready segmentation tooling
• you want modern architectures like DeepLab or Mask R-CNN

Facets

library · maturity maintenance

deep-learning computer-vision image-processing computer-vision deep-learning machine-learning image-processing python semantic-segmentation fcn caffe pascal-voc research-code cvpr-2015 linux gpu

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