I have few doubts, which is dose using embeded image give more efficiency in training the model and if so then how to embeded image and use that in vgg16 model for classification, I searched in google but didn't got any concrete which can clear my doubts, any help will be appreciated.
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I am new to deep learning. I was trying to understand the basics of image classification and followed some tutorials on MNIST data set classification. I saw various standard models used 224,224 as the image size. I got stuck at a point when it comes to image size which can be used for classification. Is it possible to use images as small as 4x4 to perform classification task in deep learning using ANN or other techniques? or is there any lower image dimensions limit which must be followed strictly? please guide me on this. Thanks in advance.
I am doing a project related to the captioning of Medical Images. I am using the Code from this link.
I am using Indiana Data set of Radiographs and using Findings as Captions for training. I trained successfully and loss value is 0.75. But my final model giving me the same caption for all the images I had checked (Some people also facing the same issue. please check the Comments of this link).
Can you please suggest me any changes in any part of the code or anything else so it starts giving me proper captions for every image I will check.
Thanks in advance.
Looking at the dataset, what I can see is that most of the data is quite similar (black and white images of Chest X rays) - please correct me if I am wrong. So what seems to be happening is that the CNN is learning common features across most of the images. The network is not just deep/advanced enough to pick out the distinguishing patterns. According to the tutorial you are following, I don't think the VGG-16 or 19 network is learning the distinguishing patterns in the images.
The image captioning model will only be as good as the underlying CNN network. If you have a class label field in your data (like the indication/impression field provided here), you can actually confirm this hypothesis by training the network to predict the class of each image and if the performance is poor you can confirm this. If you have the class label, try experimenting with a bunch of CNNs and use the one which achieves the best classification accuracy as the feature extractor.
If you do not have a class label, I would suggest trying out some deeper CNN architectures like Inception or ResNet and see if the performance improves. Hope this was helpful!
Make sure you have an equal number of images in each class. If you have 1,000 images in the “pneumonia” category and only 5 in the “broken rib” category, your model will pick the label “pneumonia” almost every time.
Please can anyone help with where I can get Haar training files that use small samples such as 5? I have downloaded a couple but one is giving me error messages while the second require 1000 samples.
Thank you very much
Little samples is not how it is designed to work with. Almost all the algorithms that lets us do classification need a large amount of training samples.
It depends on what you want to detect. If you want to detect a logo and you have a clean image of a logo, you can create many training samples out of it by adding noise, changing contrast and brightness, rotating, distorting, etc. OpenCV's Haar training module supports this, so it won't be hard.
This is called data augmentation. But if you want to detect faces, data augmentation alone won't be enough.
Creating a rule-based system by observing the few samples that you have works best for this situation, if what you want to detect is a natural object.
I can add additional links to this answer, pointing to sample code, if you can provide more details.
I am interested in the possibility of training a TensorFlow model to modify images, but I'm not quite sure where to get started. Almost all of the examples/tutorials dealing with images are for image classification, but I think I am looking for something a little different.
Image classification training data typically includes the images plus a corresponding set of classification labels, but I am thinking of a case of an image plus a "to-be" version of the image as the "label". Is this possible? Is it really just a classification problem in disguise?
Any help on where to get started would be appreciated. Also, the solution does not have to use TensorFlow, so any suggestions on alternate machine learning libraries would also be appreciated.
For example, lets say we want to train TensorFlow to draw circles around objects in a picture.
Example Inbound Image:
(source: pbrd.co)
Label/Expected Output:
(source: pbrd.co)
How could I accomplish that?
I can second that, its really hard to find information about Image modification with tensorflow :( But have a look here: https://affinelayer.com/pix2pix/
From my understanding, you do use a GAN, but insead of feeding the Input of the generator with random data during training, you use a sample Input.
Two popular ways (the ones that I know about) to make models generate/edit images are:
Deep Convolutional Generative Adversarial Networks
Back-Propagation through a pre-trained image classification model (in a similar manner to deep dream) but you can start from the final layer to feed back the wanted label and the gradient descent should be applied to the image only. This was explained in more details in the following course: CS231n (this lecture)
But I don't think they fit the circle around "3" example that you gave. I think object detection and instance segmentation would be more helpful. Detect the object you are looking for, extract its boundaries via segmentation and post-process it to make the circle that you wish for (or any other shape).
Reference for the images: Intro to Deep Learning for Computer Vision
I'm currently performing a research that involves identification of food items using image classification techniques, I'm well versed in the theories and maths of SVM, yet I'm completely lost when it comes to implementing it using Matlab.
I would like some guiding steps to perform full image classification of food, I believe it will involve color, texture, shape and size features. I just wanted to know where should I start?
Thank you very much