ethanhe42/channel-pruning

Health v2 · maintenance only

Adoption not part of the score

1088 stars · 306 forks observed · 2026-08-28

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

Reference implementation of the ICCV 2017 channel pruning method for accelerating very deep convolutional neural networks, using LASSO regression-based channel selection and least-squares reconstruction. It includes pruned VGG-16, ResNet-50, and Faster R-CNN models with 2x-5x speed-ups and pretrained releases.

Use cases

• prune channels from a trained CNN to speed up inference
• compress a deep neural network for deployment on mobile devices
• accelerate ResNet or VGG-16 with minimal accuracy loss
• speed up Faster R-CNN object detection
• reproduce results from the ICCV 2017 channel pruning paper
• compare model compression techniques for CNNs

When to choose

• you need to accelerate a pretrained VGG-16, ResNet, or Faster R-CNN with proven 2x-5x speed-ups
• you want the canonical reference implementation of LASSO-based channel pruning
• you are researching model compression and need a reproducible baseline

When to avoid

• you need actively maintained tooling for modern architectures like transformers or ViTs
• you want automated pruning via reinforcement learning (see AMC instead)
• you need a production-ready compression pipeline rather than research code

Facets

library · maturity maintenance

machine-learning deep-learning image-processing computer-vision benchmarking deep-learning computer-vision machine-learning image-processing python model-compression channel-pruning neural-network-acceleration cnn model-optimization iccv-2017 lasso-regression research-code linux gpu

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