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Core ML and Computer Vision
El documento detalla las capacidades y herramientas de Core ML de Apple, centradas en la integración de modelos de aprendizaje automático en aplicaciones. Se describen diversas funciones como el análisis de imágenes, el seguimiento de objetos y la detección de textos, así como la utilización de redes neuronales y otros modelos estadísticos. Además, se menciona la infraestructura de hardware, incluyendo componentes de los dispositivos Apple que mejoran el rendimiento en tareas de visión por computadora.
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Core ML and Computer Vision
- 1. C O RE M L A N D C O M P U T E R V I S I O N @ M I L L A N I M I X
- 2. H U NI M I X U U K M U WA N L E O N A R D O - I S R A E L M I L L Á N - G A R C Í A @millanimix • Mexicano, Tenochca, SkyAnahuacwalker • Student of the Pre-Hispanic Tradition • Rusty Researcher of Computer Vision • Objective-C developer (Oldie but goodie) • ¡Ah! I currently work as Project Manager
- 3. C O RE M L • Integrate machine learning models into your app. • A trained model is the result of applying a machine learning algorithm to a set of training data. • The model makes predictions based on new input data. https://developer.apple.com/documentation/coreml
- 4. E X AM P L E Link example
- 5. H O WD O I T D O ? C O R E M L
- 6. C O RE M L • Core ML Supports: • Image analysis. • Foundation NLP. • Learned decision trees. https://developer.apple.com/documentation/coreml
- 7. V I SI O N N AT U R A L V S A R T I F I C I A L 6 to 7 millions of cones 120 millions of rods iPhone X 12MP
- 8. V I SI O N F R A M E W O R K C O M P U T E R V I S I O N
- 9. V I SI O N F R A M E W O R K • Still Image Analysis. • Image Sequence Analysis. • Object Tracking. • Rectangle Detection. • Face Detection. • Barcode Detection. • Text Detection. • Horizon Detection. • Image Alignment. • Machine-Learning Image Analysis. • Coordinate Conversion.
- 10. C O RE M L • Core ML Tools: Python package • Machine learning models (.mlmodel): • Neural networks. • Tree ensembles. • Support vector machines. • Generalized linear models. • Takes advantages of the CPU and GPU
- 11. C O RE M L • BNNS (Basic Neural Network Subroutines) • Accelerate Framework: A collection of math functions. • CPU’s fast vector instructions. • MPSCNN: Metal Performance Shaders - Convolutional Neural Networks • Compute kernels that run on the GPU https://developer.apple.com/documentation/coreml
- 12. L E AR N I N G
- 13. N E UR A L N E T W O R K H U M A N
- 14. N E UR A L N E T W O R K A R T I F I C I A L
- 15. H U MA N N E U R A L N E T W O R K S • Neuron • Electrically excitable cell. • Receives, processes and transmits information through electrical and chemical signals. • Human Brain • 1508 g. • 86 billion neurons.
- 16. A R TI F I C I A L N E U R A L N E T W O R K S • Input layer • Hidden layers • Output layer
- 17. A 1 1B I O N I C H A R D WA R E
- 18. A 1 1B I O N I C • System on a Chip (SoC). • 64-bit ARM / 4.3 billion transistors. • iPhone 8 / Plus & X. • Two performance cores / Four high-efficiency cores. • Apple-designed GPU with three-core design.
- 21. S U MM A RY A R T I F I C I A L I N T E L L I G E N C E
- 22. S U MM A RY A R T I F I C I A L I N T E L L I G E N C E
- 23. Q U ES T I O N S ?