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!
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.
#GrimSqueaker gave you the code of this paper : https://openaccess.thecvf.com/content_eccv_2018_workshops/w33/html/Xie_Pre-training_on_Grayscale_ImageNet_Improves_Medical_Image_Classification_ECCVW_2018_paper.html
However, the model trained in it is Inception v3 not VGG16.
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.
You may find this link useful:
https://github.com/zzangho/VGG16_grayscale
Related
after making the graph embedding with Doc2vec, I want to make classification with keras, do I have to make embedding layer and put it as input to neural network or I directly use the embedding and split it into training and testing? also did the embedding layer improves the accuracy of neural network or not
How do I use a pre-trained BERT model like bert-base-uncased as weights in the Embedding layer in Keras?
Currently, I am generating word embddings using BERT model and it takes a lot of time. And I am assigning those weights like in the cide shown below
model.add(Embedding(307200, 1536, input_length=1536, weights=[embeddings]))
I searched on internet but the method is given in PyTorch. I need to do it in Keras. Please help.
I want to extract features using a pretrained CNN model(ResNet50, VGG, etc) and use the features with a CTC loss function.
I want to build it as a text recognition model.
Anyone on how can i achieve this ?
I'm not sure if you are looking to finetune the pretrained models or to use the models for feature extraction. To do the latter freeze the petrained model weights (there are several ways to do this in PyTorch, the simplest being calling .eval() on the model), and feed the logits from the last layer of the model to your new output head. See the PyTorch tutorial here for a more in depth guide.
Is there a way I can train an autoencoder model using a pre-trained model like ResNet?
I'm trying to train an autoencoder model with input as an image and output as a masked version of that image.
Is it possible to use weights from a pretrained model here?
Yes! you can definitely do transfer learning using a pre-trained network, i.e. ResNet50 as the encoder in an autoencoder. For reference, check out the following link. https://github.com/hsinyilin19/ResNetVAE
From what I know, there is no proven method to do this. I'd train the autoencoder from scratch.
In theory, if you find a pre-trained CNN which does not use max pooling, you can use those weights and architecture for the encoder stage in your autoencoder. You can also extract features from a pre-trained model and concatenate/merge them to your autoencoder. But the value add is not clear, and the architecture might become overly complex.
Keras Applications provide implementations of some of the most popular model architectures with weights pretrained on some of the most popular datasets. These predefined models are very handy for transfer learning of problems which are similar to the datasets the models were trained on.
But what if I have a very different problem and want to completely train the models on the new dataset? How can I use the models in Applications for training from scratch based on my own dataset, if I dont have pretrained weights?
You can assign a None to the weights variable, for instance with the inception V3 architecture.
keras.applications.inception_v3.InceptionV3(include_top=False, weights='None', input_shape=input_shape = (img_width, img_height, 3))
include_top=False will allow you to train the top layer with your custom network.
weights='None' means that we are training without any weights if you want to train using imagenet weight you set it to weights='imagenet'