I want to ask can I implement dlib c++ library do detect not face shape but train to detect another shapes?
Thank you for your answers
First prepare your data using this tool : imglab
Then train your own shape predictor using: train shape predictor
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Can I change the weights in a CNN like tensorflow? Or the weights of the images are fixed?
In Tensorflow it is possible, depending on the version and on the layer you use. It is even possible for class weights in the fitting method. Best is you read it in the Documentation for your used Version:-)
I am looking to train a large face identification network. Resnet or VGG-16/19. TensorFlow 1.14
My question is - if I run out of GPU memory - is it valid strategy to train sets of layers one by one?
For example train 2 cnn and maxpooling layer as one set, then "freeze the weights" somehow and train next set etc..
I know I can train on multi-gpu in tensorflow but what if I want to stick to just one GPU..
The usual approach is to use transfer learning: use a pretrained model and fine-tune it for the task.
For fine-tuning in computer vision, a known approach is re-training only the last couple of layers. See for example:
https://www.learnopencv.com/keras-tutorial-fine-tuning-using-pre-trained-models/
I may be wrong but, even if you freeze your weights, they still need to be loaded into the memory (you need to do whole forward pass in order to compute the loss).
Comments on this are appreciated.
I have a Tensorflow model that works reasonably well for detecting an object in an image and generating a bounding rectangle. The output includes one Softmax and 4 analog values for location. I need to add one more analog output for predicting the object orientation. How can I import the pre-trained model weights and freeze them so that only the part dealing with the orientation in the last layer will be trained.
I am having tensoflow frozen graph (.pb). I want to visualize the hidden layer output (feature maps) given by that graph from an image. Is there any way to do it?
Yes. Probably the best place to start is tensorflow's native way to visualize network infromation, tensorboard.
There is a set of images, each of which contains different shape entities, such as shown in the following figure. I am trying to localize and recognize these different shapes. For instance, adding a bounding box for each different shape and maybe even label it. What are the major research papers/deep learning models that have been able to solve this kind of problem?
Object detection papers such as rcnn, faster rcnn, yolo and ssd would help you solve this if you were bent on using a deep learning approach.
It’s easy to say this is a trivial problem that can be solved with tools in OpenCV and deep learning is overkill, but I can see many reasons to use deep learning tools and that does not answer your question.
We assume that your shapes has different scales and rotations. Actually your main image shown above is very large for training process and it needs a lot of training samples to generate a good accuracy at the end on test samples. In this case it is better to train a Convolutional Neural Network on a short images (like 128x128) with only one shape per each image and then use slide trick!
This project will have three main steps:
Generate test and train samples, each image should have only one shape
Train a classifier to recognize a single shape within each input image
Use slide trick! Break your original image containing many shapes to overlapping blocks of size 128x128. Pass each block to your model trained in the second step.
In this way at the end you will have label for each shape from your trained model, and also you will have location of each shape using slide trick.
For the classifier you can use exactly CNN structure of Tensorflow's MNIST tutorial.
Here is a paper with exactly same method applied to finger print images to extract local features.
A direct fingerprint minutiae extraction approach based on convolutional neural networks