Gumpest/YOLOv5-Multibackbone-Compression

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1020 stars · 197 forks observed · 2026-08-28

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

A YOLOv5-based toolbox for swapping in lightweight or high-accuracy backbones (TPH-YOLOv5, GhostNet, ShuffleNetV2, MobileNetV3-Small, EfficientNetLite, PP-LCNet, Swin Transformer) and applying compression techniques like pruning (EagleEye, Network Slimming) and quantization (MQBench), with deployment support for TensorRT and ncnn. It is primarily demonstrated on the VisDrone drone detection dataset.

Use cases

• compress a YOLOv5 model for edge deployment
• replace YOLOv5 backbone with a lightweight network like GhostNet or MobileNetV3
• prune a YOLOv5 detection model with EagleEye or network slimming
• quantize YOLOv5 for TensorRT or ncnn inference
• train TPH-YOLOv5 on drone imagery like VisDrone
• build a Swin Transformer YOLOv5 variant for higher accuracy

When to choose

• you need to shrink YOLOv5 models for mobile or embedded inference
• you want to benchmark multiple lightweight backbones within the YOLOv5 framework
• you are working on small-object detection such as drone-view datasets
• you want a single repo covering pruning, quantization, and deployment for YOLOv5

When to avoid

• you need a maintained project with active releases and a clear license
• you use detection frameworks other than YOLOv5
• you need production-grade support or commercial licensing
• you want a plug-and-play tool rather than research code requiring manual setup

Facets

library · maturity maintenance

machine-learning deep-learning image-processing computer-vision benchmarking computer-vision deep-learning machine-learning image-processing python yolov5 object-detection model-compression pruning quantization tensorrt ncnn lightweight-backbones tph-yolov5 visdrone linux gpu

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