PengyiZhang/SlimYOLOv3

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1152 stars · 315 forks observed · 2026-08-28

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

SlimYOLOv3 is a research implementation of channel-pruned YOLOv3 object detectors optimized for real-time inference on UAVs and embedded devices. It provides a training pipeline (normal training, L1 sparsity training, channel pruning, fine-tuning) built on PyTorch and darknet, evaluated on the VisDrone2018-Det benchmark.

Use cases

• run real-time object detection on drones with limited compute
• prune YOLOv3 channels to reduce FLOPs and parameters
• train a sparse object detector with L1 regularization on channel scaling factors
• fine-tune a pruned YOLOv3 model to recover accuracy
• evaluate pruned YOLOv3 models on the VisDrone dataset
• deploy lightweight object detection on embedded devices

When to choose

• you need a faster, smaller YOLOv3 for UAV or embedded real-time detection
• you want to apply channel pruning to YOLOv3 with a proven recipe
• you are working with the VisDrone aerial detection benchmark

When to avoid

• you need a maintained, production-ready detection framework (last release 2019, no license)
• you need detection on general images rather than aerial/UAV scenes
• you want plug-and-play pretrained models without running the pruning pipeline yourself
• you require a permissively licensed project for commercial use

Facets

library · maturity maintenance

machine-learning computer-vision image-processing deep-learning computer-vision machine-learning deep-learning python object-detection yolo channel-pruning uav drone model-compression pytorch darknet real-time linux gpu

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markdown · JSON · MCP: product_card(name="PengyiZhang/SlimYOLOv3")