DeepWisdom/AutoDL

Health v2 · maintenance only

Adoption not part of the score

1196 stars · 214 forks observed · 2026-08-28

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

AutoDL is a fully automated deep learning framework from DeepWisdom that won the NeurIPS AutoDL challenge. It performs automatic feature engineering, model selection, and hyperparameter tuning for multi-label classification across image, video, audio, text, and tabular data with no human intervention.

Use cases

• automatically classify images without writing model code
• build a text classifier with zero manual tuning
• train a tabular data classifier automatically
• classify audio or video datasets hands-free
• run automated machine learning on small datasets
• compare automated models across multiple modalities

When to choose

• you need fully automated classification across any data modality
• you lack ML expertise and want no manual feature or model tuning
• you need fast baseline models in seconds
• you want a proven competition-winning AutoML pipeline

When to avoid

• you need cutting-edge, actively maintained AutoML tooling
• your task is regression or unsupervised learning rather than classification
• you require fine-grained manual control over architectures
• you need support for recent Python or CUDA versions

Facets

library · maturity maintenance

machine-learning deep-learning data-science machine-learning deep-learning artificial-intelligence data-science python automl nas neural-architecture-search feature-engineering model-selection multi-label-classification autodl-challenge lightgbm pytorch tensorflow linux gpu

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