There are manual tools like Makesense.ai to create the labelled data for custom training YOLO, but is there any method for automatic labelling of multiple objects in an image so that the image labelling process would be faster?
Makesense.ai has the very feature you are asking for. You can use a network trained on coco or provide your own yolov5 tensorflow js model.
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Hi I have trained object detection model using tensorflow 1.14 object detection API, my model is performing well. However, I want to reduce/optimize parameters of model to make it lighter. How can I use pruning on trained model?
Did you check the Pruning guide on the Tensorflow website ?
It has concrete examples on how to prune a model and benchmark the size and performance improvements.
The link here describes a method for image classification using affinity propagation. I'm confused as to how they got the feature vectors, i.e, the data structure of the images, e.g, arrays?
Additionally, how would I accomplish this given that I can't use Places365 as it's custom data (audio spectrograms)?
Finally, how would I plot the images as they've done in the diagram?
The images are passed through a neural network. The activations of neural network layer for an image is the feature vector. See https://keras.io/applications/ for examples.
Spectrograms can be treated like images.
Sometimes even when domain is very different, the neural network features can extract useful information that can help you with clustering/classification tasks.
I am trying to train image classificator using CNN GoogleNet Inception. I have some labeled images(cca 1000 per category) and much more unlabeled images. So far have I used just labeled images and I got good accuracy. I am just not sure if it is possible to use somehow unlabeled images.
The only information about them is, that there are always some images(1-10) in one directory. And images in one directory belong to same class.
Thank You
Have a look at Keras ImageDataGenerator. Its a convenience function that reads images from subdirectories that correspondend to classes.
Even if you don´t use Keras for training you could do a dummy run to generate labels for your unlabeled images and then use these in your neural network architecture.
You can also look into pseudo labelling for images for which you don´t have any information regarding the content.
Some users might see this as opinion-based-question but if you look closely, I am trying to explore use of Caffe as a purely testing platform as opposed to currently popular use as training platform.
Background:
I have installed all dependencies using Jetpack 2.0 on Nvidia TK1.
I have installed caffe and its dependencies successfully.
The MNIST example is working fine.
Task:
I have been given a convnet with all standard layers. (Not an opensource model)
The network weights and bias values etc are available after training. The training has not been done via caffe. (Pretrained Network)
The weights and bias are all in the form of MATLAB matrices. (Actually in a .txt file but I can easily write code to get them to be matrices)
I CANNOT do training of this network with caffe and must used the given weights and bias values ONLY for classification.
I have my own dataset in the form of 32x32 pixel images.
Issue:
In all tutorials, details are given on how to deploy and train a network, and then use the generated .proto and .caffemodel files to validate and classify. Is it possible to implement this network on caffe and directly use my weights/bias and training set to classify images? What are the available options here? I am a caffe-virgin so be kind. Thank you for the help!
The only issue here is:
How to initialize caffe net from text file weights?
I assume you have a 'deploy.prototxt' describing the net's architecture (layer types, connectivity, filter sizes etc.). The only issue remaining is how to set the internal weights of caffe.Net to pre-defined values saved as text files.
You can get access to caffe.Net internals, see net surgery tutorial on how this can be done in python.
Once you are able to set the weights according to your text file, you can net.save(...) the new weights into a binary caffemodel file to be used from now on. You do not have to train the net if you already have trained weights, and you can use it for generating predictions ("test").
I am doing a project on Writer Identification. I want to extract HOG features from Line Images of Arabic Handwriting. And than use Gaussian Mixture Model for Classification.
The link to the database containing the line Images is : http://khatt.ideas2serve.net/
So my questions are as follows;
There are three folders namely Test, Train and Validate. So, from which folder do I need to extract the features. And for what purpose should we use each of the folders.
Do we need to extract the features from individual images and merge them or is there any method to extract features of all the images together.
Test, Train and Validate
Read this stats SE question: What is the difference between test set and validation set?
This is basic machine learning, so you should probably go back and review your course literature, since it seems like you're missing some pretty important machine learning concepts.
Do we need to extract the features from individual images and merge them or is there any method to extract features of all the images together.
It seems, again, like you're missing basic concepts here. Histogram of oriented gradients subdivides the image and finds the oriented gradient. See this SO question for examples of hos this looks.
The traditional way of using HoG is: for each image in your training set, you extract the HoG, use these to train a SVM, validate the training with the validation set, then actually use the trained SVM on the test set.
You need to extract the HOG features from each image separately. Furthermore, you have to resize all images to be of the same size, otherwise all your HOG vectors will be of different length.
You can use the extractHOGFeatures function in MATLAB. See this example.