Core ML is optimized for on-device performance of a broad variety of model types by leveraging Apple Silicon and minimizing memory footprint and power consumption.

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• Core ML Models
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  • FastViT: Image Classification
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  • Depth Anything: Depth estimation
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  • DETR Resnet50: Semantic Segmentation
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  • Additional Core ML Model Gallery Models
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  • Stable Diffusion Core ML models
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  • Hugging Face Core ML Examples
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  \n
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Apple Machine Learning Research

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Open research to enable the community to deliver amazing experiences that improve the lives of millions of people every day.

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• Models
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  • OpenELM: open, Transformer-based language model. Base | Instruct
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  • MobileCLIP: Mobile-friendly image-text models.
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  \n
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• Datasets
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  • FLAIR: A large image dataset for federated learning.
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  • DataCompDR: Improved datasets for training image-text models.
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  \n
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• Benchmarks
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  • TiC-CLIP: Benchmark for the design of efficient continual learning of image-text models over years
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  \n
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Select Highlights and Other Resources

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• Hugging Face CoreML Examples – Run Core ML models with two lines of code!
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• Apple Model Gallery
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• New features in Core ML Tools 8
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• Apple Core ML Stable Diffusion – Library to run Stable Diffusion on Apple Silicon with Core ML.
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• Hugging Face Blog Posts
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  • Releasing Swift Transformers: Run On-Device LLMs in Apple Devices (Aug, 2023)
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  • Faster Stable Diffusion with Core ML on iPhone, iPad, and Mac
  \n
  • Using Stable Diffusion with Core ML on Apple Silicon
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  \n
\n

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