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I have found the VGG16 network pre-trained on the (color) imagenet database (as .npy). Is there a VGG16 network pre-trained on a gray-scale version of the imagenet database available?
(The usual 'tricks' for using the 3-channel filters of the conv1.1 layer on the gray 1-channel input are not enough for me. I am looking at incremental improvements of the network performance, so I need to see how the transfer learning behaves when the pre-trained model was 'looking' at gray-scale input).
Thanks!
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You have two options: Use a colored pre-trained VGG16 model and duplicate one channel to the three channels. Train your VGG16 model on the ImageNet grayscaled dataset.
VGG16 is a convolutional neural network trained on a subset of the ImageNet dataset, a collection of over 14 million images belonging to 22,000 categories. K. Simonyan and A. Zisserman proposed this model in the 2015 paper, Very Deep Convolutional Networks for Large-Scale Image Recognition.
Very Deep Convolutional Networks for Large-Scale Image Recognition(VGG-16) The VGG-16 is one of the most popular pre-trained models for image classification.
The 16 in VGG16 refers to it has 16 layers that have weights. This network is a pretty large network and it has about 138 million (approx) parameters.
Yes, there's this one: https://github.com/DaveRichmond-/grayscale-imagenet
Greyscale imagenet trained model, and also a version of it that's finetuned on X-rays. They showed that Imagenet performance barely drops btw.
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