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Showing posts from March 3, 2023

How to Fine-Tune DeiT: Data-efficient Image Transformer

If you're interested in the latest advances in deep learning for computer vision, you may have heard about DeiT, or the Data-efficient Image Transformer . DeiT is a state-of-the-art model for image classification that achieves impressive accuracy while using fewer training samples than its predecessors. In this blog post, we'll take a closer look at DeiT and how you can implement and fine-tune it in TensorFlow. What is DeiT? DeiT is a model developed by researchers at META AI that builds on the success of the Transformer architecture , which was originally developed for natural language processing tasks. Like the Transformer, DeiT uses self-attention to process input data, allowing it to capture complex relationships between image features. However, DeiT is specifically designed for image classification tasks, and achieves this by using a novel distillation-based training method that enables it to be trained on smaller datasets than previous models. The key innovation behind

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