mit-han-lab/proxylessnas

Health v2 · maintenance only

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1447 stars · 281 forks observed · 2026-08-28

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

ProxylessNAS is a neural architecture search (NAS) framework that directly searches CNN architectures on the target task and target hardware (CPU, GPU, or mobile) without a proxy. It also provides pretrained, hardware-specialized image classification models loadable via PyTorch Hub.

Use cases

• search neural network architectures optimized for a specific hardware platform
• get a pretrained efficient image classification model for mobile deployment
• reduce inference latency of CNNs on embedded devices
• run hardware-aware AutoML experiments in PyTorch
• benchmark latency-accuracy tradeoffs against MobileNetV2 and MnasNet

When to choose

• you need architectures specialized for a particular device or latency budget
• you want efficient pretrained models for on-device image classification
• you are reproducing or building on the ProxylessNAS paper

When to avoid

• you need actively maintained NAS tooling with broad framework support
• you are searching architectures for non-vision tasks
• you need a production AutoML pipeline rather than research code

Facets

library · maturity maintenance

machine-learning deep-learning llm-training machine-learning computer-vision gpu-computing artificial-intelligence python cpp windows neural-architecture-search automl hardware-aware-nas efficient-inference image-classification on-device-ai research-code iclr-2019 gpu linux macos

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