LLM-1 Project Bootcamp: Computer Vision & Document AI
Автор: SupportVectors
Загружено: 2024-10-26
Просмотров: 265
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Speaker: Asif Qamar [ / asifqamar ]
SupportVectors AI Training Lab [https://supportvectors.ai]
In this session of " LLM -1 Projects Bootcamp," the following content was covered;
Teacher-Student Distillation for Efficient Training: In the "DeiT" model, a CNN "teacher" guides a Transformer "student" using a distillation loss that helps the student learn image classification with fewer data and reduced computational requirements.
Loss Functions and Hyperparameters: Cross-entropy loss focuses on real-world classifications, while distillation loss aligns the student model’s outputs with the teacher’s. A lambda hyperparameter balances the influence of each, with temperature scaling used to refine probability alignment.
Democratizing Vision Transformers: The "DeiT" model's innovations allow Vision Transformers to be trained on limited data and standard hardware, expanding access to high-performance models without extensive datasets or specialized hardware.
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