rgeirhos/texture-vs-shape
Pre-trained models, data, code & materials from the paper "ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness" (ICLR 2019 Oral)
This project helps researchers understand how ImageNet-trained convolutional neural networks (CNNs) perceive objects, specifically whether they rely more on an object's texture or its shape. You can input an image, and the system outputs a classification decision alongside an analysis of the model's 'shape bias'. This is primarily for computer vision researchers and cognitive scientists studying deep learning model behavior.
811 stars. No commits in the last 6 months.
Use this if you are a researcher investigating the internal workings of CNNs and want to analyze or replicate experiments related to texture vs. shape bias in image recognition models.
Not ideal if you are looking for a general-purpose image classification tool or a method to immediately improve the performance of your existing models without deeper research.
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R
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Last pushed
May 02, 2023
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