The model definition.pic.twitter.com/YMGwBhHCHu
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Training an Xception-style image segmentation model on the Oxford Pets dataset, in about a hundred lines end-to-end -- on GPU, for free via Colab. https://colab.research.google.com/drive/1EJKlMKrdlZ5BctIWlk3TrXJfE7dzpiaH …pic.twitter.com/UwS9Q0VJWi
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A good computationally light alternative of heavy inceptionV3 model is Xception.
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Did you notice any decrease in the computation speed since Google colab changed its GPU and CPU?
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Awesome building blocks for CNN. I’m developing DL models for segmenting Organ at risk on CT scans by borrowing archectures from real-time semantic segementation field. The most recent ones, such as BiSeNet and DAFnet, are using light-weight Xception as their backbones, thanks2u
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