shekkizh/FCN.tensorflow

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1248 stars · 520 forks observed · 2026-08-28

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

A TensorFlow implementation of Fully Convolutional Networks (FCN) for semantic segmentation, based on the reference code from the original paper authors. It was trained on the MIT Scene Parsing Challenge dataset and includes training, visualization, and debug tooling.

Use cases

• implement fcn for semantic segmentation in tensorflow
• train a scene parsing model on mit ade20k dataset
• learn how fully convolutional networks work with example code
• segment objects in images with a pretrained fcn model
• reference implementation of fcn paper in tensorflow

When to choose

• you need a simple, readable TensorFlow 0.x implementation of FCN for learning purposes
• you want to reproduce the FCN paper on the MIT Scene Parsing dataset
• you are studying semantic segmentation model architecture and training details

When to avoid

• you need support for modern TensorFlow versions (2.x) or current Python
• you need a production-ready or actively maintained segmentation solution
• you want state-of-the-art segmentation accuracy with modern architectures

Facets

library · maturity abandoned

machine-learning deep-learning image-processing computer-vision computer-vision image-processing deep-learning machine-learning python cross-platform semantic-segmentation fully-convolutional-networks tensorflow scene-parsing fcn jupyter-notebook gpu linux

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markdown · JSON · MCP: product_card(name="shekkizh/FCN.tensorflow")