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JiahuiYu / slimmable_networks

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reproducing CIFAR10 results for AutoSlim

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RudyChin
RudyChin commented Jun 25, 2020

Hi Jiahui,

Thanks for the great work. I'm trying to reproduce AutoSlim for CIFAR-10 (Table 2).
Could you please provide a detailed hyperparameter you used for it?

I'm able to train the baseline MobileNetV2 1.0x to 7.9 Top-1 error using the following hyperparameters:

  • 0.1 initial learning rate
  • linear learning rate decay
  • 128 batch size
  • 300 epochs of training
  • 5e-4 weight decay
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good first issue

Byron / dua-cli

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parallel deletion (without following symlinks) using jwalk

Byron
Byron commented Mar 15, 2020

Previously jwalk would follow symlinks, so we could not use it and instead implemented our own traversal.

Now with jwalk 0.5, this is supported and we can use our standard jwalker to do the job.

https://github.com/Byron/dua-cli/blob/543f7f3948c26250a8fc6ebf79a49f3ddfa3cb63/src/interactive/app/handlers.rs#L305-L340

The function above would have to change to use jwalk. It should be straight

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enhancement good first issue help wanted

mehtadushy / SelecSLS-Pytorch

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Reference ImageNet implementation of SelecSLS CNN architecture proposed in the SIGGRAPH 2020 paper "XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera". The repository also includes code for pruning the model based on implicit sparsity emerging from adaptive gradient descent methods, as detailed in the CVPR 2019 paper "On implicit filter level sparsity in Convolutional Neural Networks".

sparsity deep-learning efficient cnn pytorch imagenet pruning siggraph pytorch-implementation cvpr2019 efficient-architectures
  • Updated Jul 23, 2020
  • Python

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