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I am trying to create Deep Neural Network based classifier for chest x-ray to check there is TB or not. I read that transfer Learning technique can be used for this using inception model v3. My question is inception model is created by training with imagenet(physical object) right? How can this be used for medical image training?
One intuition is that physical objects and medical images do share some similarities especially in low-level features such as edges, curves and small object regions.
Experiments indicate that pretraining a network on ImageNet can benefit most computer vision tasks even if the images from the target domain look very different from what are in the ImageNet.
To achieve best performance, you can use a pretrained network from Imagenet and fine-tune the last layer or all layers with small learning rates on your dataset.
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I want to use a pre-trained Unet model using segmentation_models API for the Cityscapes dataset, but I need the pre-trained weights for the same. Where can I find the pre-trained weights for a Unet model trained on the Cityscapes dataset?
Please guide me on this!!!
UNet is absent from the benchmark so i assume it is not adapted for this dataset (too slow and not enough performant probably). However, I advise you to start with DeepLabv3+ from Google which is not so complicated and more adapted for this dataset.
You can use this repository where it is implemented, well documented and useable with pretrained weights from cityscape dataset (and also PascalVOC dataset).
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I want to use my VAE trained on an image dataset as a feature extractor for another task, so that I could for example replace a ResNet for feature extraction with my VAE.
Which Layers do I use for this?
With "standard" autoencoders you just take the encoding network, but since the latent layer of the VAE consist of mean and distribution I do not know which layers I should use for feature extraction.
Does somebody know how to use a VAE as a feature extractor and what to consider with using different components?
Hidden variables z are used in VAEs as the extracted features for dimensionality reduction. Here is an example dimensionality reduction from four features in the original space ([x1,x2,x3,x4]) to two features in the reduced space ([z1,z2]) (source):
Once you have trained the model, you can pass a sample to the encoder it extracts the features. You may find a Keras implementation example on mnist data here (see the plot_label_clusters function):
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If one image is a part of another image, then how to compute the accurate location in deep learning way?
Now I could compute this by extracting and matching key points using OpenCV, but I hope to solve it with neural networks.
Any ideas to design the networks and loss functions?
Thanks very much.
This is a detection problem. The simplest approach to do it is to create a a network with two heads, one for classification and the other for the bounding box (regression).
you feed your network with the image and respective label, and sum the lossess and do a backward. train for some epochs and you'll get your self a detection model that you can use to detect what you need. but its just a simple approach and it can get much more complex.
You may as well skip this and use an existing detection architecture or better framework which simplifies your life much better.
For Tensorflow I belive you can use ObjectDetctionAPI and for Pytorch you can use Detectron, Detectron2, mmdetection among others.
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I am new in the deep learning, and I want to use convolutional neural networks (CNN) for image recognition (biometric images).
I would like to use pre-trained CNN architecture and use a python programming language.
How can I select the suitable CNN architecture (VGGNet or GoogleNet ...), is there a preferable CNN architecture?
What do you think is the best library to do this work, how can I select the suitable library?
Thanks..
You can use tensorflow-slim. They have a library of many top pre-trained CNN models that you can use directly or fine-tune easily on your dataset. I think the training time depends on your hardware and amount of data you have.
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Hi I'm new to deep learning and convolutional neural network. Could someone please explain the problem in the figure below? Someone told me that the fluctuation of validation accuracy is the problem here. But I don't quite understand the negative effect of this fluctuation. Why don't we just look at the last point of the figure?
enter image description here
When training a deep learning module you have to validate it.
Which means you are showing the unseen data to algorithm.
So validation accuracy can be less that the training accuracy. Because there's an scenario called over-fitting. Where your training algorithm is too much attached to training data and does not generalize well to other unseen data.
On the fluctuating issue it can be normal. Because we training and testing the algorithm is a stochastic manner.