mit-han-lab/once-for-all

Health v2 · maintenance only

Adoption not part of the score

1956 stars · 344 forks observed · 2026-08-28

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

Once-for-All (OFA) is a PyTorch library implementing the ICLR 2020 Once-for-All network, which trains a single supernet that can be specialized into many efficient sub-networks for different devices and latency constraints. It provides a pretrained model zoo, progressive shrinking training, and sub-network sampling/search for efficient edge deployment.

Use cases

• deploy image classification models on mobile and edge devices
• run neural architecture search without retraining from scratch
• specialize one trained network for many hardware targets
• compress a model by depth, width, kernel size, and resolution
• achieve high ImageNet accuracy under mobile latency budgets
• benchmark efficient models on CPU, DSP, and FPGA

When to choose

• you need efficient vision models for diverse edge hardware without per-device retraining
• you want pretrained MobileNetV3/ResNet50-style supernet weights to sample sub-networks from
• you are researching NAS, AutoML, or model compression

When to avoid

• you need general-purpose model training unrelated to architecture search
• you want actively developed tooling - the project is research code with infrequent updates
• your target is LLM or generative model efficiency rather than vision classification

Facets

library · maturity maintenance

machine-learning deep-learning llm-training machine-learning deep-learning computer-vision embedded-systems gpu-computing python cross-platform neural-architecture-search automl model-compression tinyml edge-ai progressive-shrinking efficient-inference imagenet pytorch gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="mit-han-lab/once-for-all")