leaderj1001 / Mixed-Depthwise-Convolutional-Kernels

Implementing MixNet: Mixed Depthwise Convolutional Kernels using Pytorch

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Training MixNet models for Cifar 10

meteozay opened this issue · comments

Hello,

Have you trained MixNet models for Cifar-10 from scratch (not transfer learning from ImageNet models)?

With default configuration, I obtain about 70% accuracy for MixNet small, and the accuracy decreases as the model size increases. Do I need to change some particular parameters in the configuration while training Cifar-10?

Thank you.

Hi, I also have the same problem which my Mixnet was originally installed from pip install Keras_Mixnet. I wonder if your code is similar to Keras_Mixnet.

So how many epoch did you set to get the over 90% of validation accuracy? I initially set the number of epoch as 10,000 and the number of patient as 50. However, it stopped between from 110th to 120 epoch. So, the result is 73% of validation accuracy. But, I am planning to change the number of patient from 50 to 100 in order to get the same validation accuracy. I am looking forward to have your instruction.
Thank you!

The code currently uploaded to github is model architecture in the ImageNet dataset. ImageNet and CIFAR have different input image sizes, so you need to train by modifying the stem layer and model architecture.
Thank you.