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New code walkthrough on http://keras.io : a reproduction of the ConvMixer architecture from the "Patches are all you need" paper. Implemented by @RisingSayakhttps://keras.io/examples/vision/convmixer/ …
I think it's fun that vision models are converging towards an architecture that is nearly indistinguishable from Xception (back from 2016). Depthwise separable convolutions are simply the correct architecture prior for spatial perception, and it's time they become mainstream.
Totally agreed.
Would it make sense to use this model for image segmentation by replacing the last 2 layers (pooling/dense) by a conv?
I am gonna try and implement this
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