To save server space I convert keras models (h5) to tflite format and I'd like to create heatmaps / saliency maps with the tflite models. Its easy to create a heatmap with a keras model, but tflite I have not seen any examples. Is it possible to do this?
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I am trying to run an image classification model for radiographic images, that have only 1 channel. I was interested in using transfer learning to evaluate how models pre-trained on the ImageNet dataset will perform for radiographic images.
However, when trying to use the keras.applications.Xception() class, it states that only allows 3-channel images.
Looked for another model, but it seems like all the pre-trained models available in Keras require 3-channel images.
Do you have any suggestion in order to be able to use these models?
Thanks in advance.
I am new in Pytorch. My question is: How do I apply transfer learning to a custom dataset? I am doing image segmentation on brain tumors. I can find examples which use U-net structure but I could not find examples using weights of the pre-trained models for a U-net image segmentation?
You could obtain pre-trained models in two ways:
Model weights or complete models shared in formats such .pt or .pth:
In this case, Saving and Loading Models is a good starting point. Copying from the tutorial there, you could load a model as
model = TheModelClass(*args, **kwargs)
model.load_state_dict(torch.load(PATH))
The other way is to load the model from torchvision. A list is available models is available at Torchvision Models. U-Net is not available yet. However, it is possible to load a pre-trained model as the encoder and write a separate decoder to form a U-Net with a pre-trained encoder.
In this case, the model object returned from the function calls shown in the API are already loaded with pretrained weights when pretrained=True.
For writing a custom dataloader, PyTorch data loaders may be a useful guide.
I am using the DenseNet121 CNN in the Keras library and I would like to visualize the features maps when I predict images. I know that is possible with CNN we have made on our own.
Is it the same thing for models available in Keras like DenseNet?
I have a gradient boost model saved in the .pkl format. I have to load this model in tensorflowjs. i can see that there is a way to load a keras model but I can't find a way to load a sklearn model. Is it possible to do this?
It is not possible to load sklearn model in tensorflow.js. Tensorflow.js allows to load models written in tensorflow.
Though, I haven't tried myself, but I think that you can possibly use the scikit learn wrapper to rewrite the classifier in tensorflow. The model can be saved and converted to a format that can be loaded in tensorflow.js.
I'm using faster_rcnn_resnet50 to train a model which will detect corrosions in images and I want to train a model from scratch instead of using transfer learning.
I don't know if this right but the reason I want to do this is that the already existing weights (which are trained on COCO) will affect my model trained on corrosion images.
One way I would like to do this is randomize or unfreeze the weights of the feature extractor on the resnet50 and then train the model on my images.
but there's no function or an option in the resnet50 config file to randomize or unfreeze weights.
I've made a new labelmap with a single label and tried it with transfer learning. It's working but I would like to have a model is trained just on my images and the previous weights shouldn't affect my predictions.
This is the first time I'm working with object detection and transfer learning. Will the weights of the pre-trained model on COCO affect my model which is trained on custom images of corrosion? How do you use tensorflow object-detection API without transfer learning?