tanluren/yolov3-channel-and-layer-pruning

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1515 stars · 441 forks observed · 2026-08-28

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

A Python toolkit built on ultralytics/yolov3 that implements channel pruning, layer pruning, and knowledge distillation for YOLOv3/v4 (including SPP and tiny variants) object detection models. It uses BN-layer gamma coefficients for network slimming to compress model depth and width, improving inference speed on custom datasets.

Use cases

• compress a yolov3 model for faster inference on edge devices
• prune channels from a trained yolov4 detector
• apply knowledge distillation to recover accuracy after pruning
• run sparse training to shrink BN gamma coefficients before pruning
• search for a smaller yolov3-tiny model for a custom dataset
• speed up object detection for drone imagery like visdrone

When to choose

• you need to shrink YOLOv3/v4 detection models for deployment on resource-constrained hardware
• you want pruning plus distillation in one workflow based on darknet-style YOLO configs
• you want to compress a custom-dataset detector while keeping reasonable mAP

When to avoid

• you use newer architectures like YOLOv5/v8 or transformer detectors
• you need actively maintained tooling - the project has not seen releases since 2020
• you want one-click compression without tuning sparse training hyperparameters

Facets

library · maturity maintenance

machine-learning deep-learning llm-training computer-vision deep-learning machine-learning python yolov3 yolov4 model-compression channel-pruning layer-pruning knowledge-distillation object-detection network-slimming sparse-training linux gpu

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