innat/HybridModel-GradCAM
A Keras implementation of hybrid efficientnet swin transformer model.
This project helps machine learning engineers and researchers visually interpret how a hybrid AI model makes decisions on image data. It takes an image and a trained hybrid model (combining EfficientNet and Swin Transformer) as input and outputs visual heatmaps, highlighting the specific regions within the image that the model focused on to reach its conclusions. This is useful for understanding and debugging complex image classification or recognition tasks.
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Use this if you need to understand which parts of an image your hybrid EfficientNet-Swin Transformer model prioritizes when making predictions.
Not ideal if you are working with non-image data or need to interpret a different type of AI model (e.g., a pure CNN, a recurrent neural network, or a large language model).
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Apache-2.0
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Last pushed
Oct 14, 2023
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