Rishit-dagli/Transformer-in-Transformer
An Implementation of Transformer in Transformer in TensorFlow for image classification, attention inside local patches
This is a TensorFlow implementation of the Transformer in Transformer model for image classification. It processes images by applying attention at both the pixel and broader patch levels to identify and categorize objects within the image. It's designed for machine learning engineers and researchers focused on computer vision tasks.
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Use this if you are a machine learning engineer or researcher who needs to implement an advanced image classification model using TensorFlow, particularly for improving recognition accuracy through detailed pixel and patch analysis.
Not ideal if you are looking for a plug-and-play solution without any coding, or if your primary framework is PyTorch.
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Jupyter Notebook
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Apache-2.0
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Last pushed
Feb 12, 2022
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