megvii-model/ShuffleNet-Series

Health v2 · maintenance only

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1513 stars · 272 forks observed · 2026-08-28

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

A collection of ShuffleNet-series efficient convolutional neural network models (V1, V2, V2+, Large, ExLarge) plus NAS-derived backbones like OneShot and DetNAS from Megvii Research. It provides PyTorch model definitions and pretrained weights for image classification and detection backbones.

Use cases

• build an image classifier with a lightweight CNN
• run inference on mobile or edge devices with efficient models
• get pretrained ShuffleNetV2 weights for transfer learning
• compare efficient backbone accuracy against MobileNet
• use a NAS-searched backbone for object detection
• reproduce ShuffleNet paper results

When to choose

• you need fast, low-FLOPs image classification backbones
• you want pretrained weights for ShuffleNet variants
• you need detection backbones from DetNAS or One-Shot NAS

When to avoid

• you need transformer-based vision models
• you want actively maintained training pipelines
• you need models beyond classification and detection backbones

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision image-processing machine-learning python shufflenet image-classification model-zoo neural-architecture-search pretrained-models efficient-networks

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markdown · JSON · MCP: product_card(name="megvii-model/ShuffleNet-Series")