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.
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I Have a trained classifier: VGG16 on say Image Net (or my own images DB and classes). I want to segment my images automatically knowing there are classes on images my classifier knows. How to automate image segmentation?
For this you can extract Grad-CAM features. Kears already has published an official documentation for Grad-CAM extraction you can find it here.
So for your task steps need to followed are
Extract Grad-CAM from the images
Based on Grad-CAM create a segmentation mask using simple image processing technique
In this method you can easily create segmentation mask for images but masks may not be so accurate . Beacuse, see this picture,
it is for Xception model (ImageNet).
Hope you will understand and you will be helpful.
I have been trying to visualize and see layers/parameters/Flops for this Tensorflow model of Text-to-speech synthesis https://github.com/Rayhane-mamah/Tacotron-2
Is there a way I can visualize or see graph of Tacotron-2 with all its RNN/LSTM layers using tensorflow?
Do I need train the model first before being able to print the model, or is there a way to simply see what ops are in each layer for the model without training?
I'm having a hard time figuring this out as I'm new to TF/pytorch frameworks. It seems to me one should be able to just print the model as the github has .py source, but I just don't know how this simple/basic things work with python and how to do it.
I was wondering if anyone can help by providing me with some guidelines for creating a bald-or-not image classifier.
So far I have a model for face and eye detection and to sum it up, this is my main questions:
Where can I find datasets for this kind of classification without going to google and download thousands of images by hand?
What classification model (i.e. the structure of layers in the network) should be used for this?
Question 1:
You could start by looking at some of the datasets available in Kaggle or Tensor Flow Datasets to see if there is anything available.
If none, you could try using an Image scraper tool to download images quickly compared to by hand.
Question 2:
Typically Image Classification model uses Convolutional Layers and MaxPooling layers. On top of the commonly used Dense Layer for Multi-layer Perceptron.
To get started you can study the Tensor Flow tutorial for Image Classification in this link,
which classifies whether the Image is Cat or Dog.
This example can provide you with the general idea of how to build an Image Classifier.
Hope this helps you. Thanks
I have RGB-D (color&depth) images for given scene. I would like to use tensorflow to train a classification model based on pre-trained network such as inception. As far as I understood, these pre-trained models were built using 3-channel RGB images. However, the inclusion of 4th channel cannot be handled.
How do I use RGB-D images directly? Do I need to pre-process the images, and seperate RGB and D, if so, how do I use the D (1-channel) alone?
Thank you!
If you want to use a pre-trained model you can only use RGB, as they were only trained to understand RGB. In this case, it is as you said: separate them and discard depth.
To use a 4 channel image like this you would need to retrain the network from scratch rather than loading a pre-trained set of weights.
You will probably get good results using the same architecture as is used for 3 channel images (save for the minor change required to support the 4 channel input), so retraining shouldn't be terribly hard.
Has anyone implement the FRCNN for TensorFlow version?
I found some related repos as following:
Implement roi pool layer
Implement fast RCNN based on py-faster-rcnn repo
but for 1: assume the roi pooling layer works (I haven't tried), and there are something need to be implemented as following:
ROI data layer e.g. roidb.
Linear Regression e.g. SmoothL1Loss
ROI pool layer post-processing for end-to-end training which should convert the ROI pooling layer's results to feed into CNN for classifier.
For 2: em...., it seems based on py-faster-rcnn which based on Caffe to prepared pre-processing (e.g. roidb) and feed data into Tensorflow to train the model, it seems weird, so I may not tried it.
So what I want to know is that, will Tensorflow support Faster RCNN in the future?. If not, do I have any mis-understand which mentioned above? or has any repo or someone support that?
Tensorflow has just released an official Object Detection API here, that can be used for instance with their various slim models.
This API contains implementation of various Pipelines for Object Detection, including popular Faster RCNN, with their pre-trained models as well.