Replaces standard convolutional neural networks with optimized Vision Transformer (ViT) architectures. Utilizes weight-sharing NAS to find optimal model configurations for computer vision tasks.
Tech Stack
PythonAI, ML & Data
Why Cream?
• High accuracy on ImageNet
• Efficient search for ViT variants
• Comprehensive research codebase
Limitations
• Academic focus, less production-ready
• High memory requirements for training
• Limited documentation for non-researchers
3/5/2026
Last Update
238
Forks
34
Issues
MIT
License
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