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.
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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
Which version of YOLO-tensorflow (customised cnn like googlenet) is preferred for traffic science?
If the training datasets are blurred and are with noise is that okay to train or what are the steps to be considered for training dataset images?
You may need to curate your own dataset using frames from a traffic camera and manually tagging images with cars where the passengers' seatbelts are or are not buckled, as this is a very specialized task. From there, you can do data augmentation (perhaps using the Keras ImageDataGenerator class). If a human can identify a seatbelt in an image that is blurred or noisy, a model can learn from it. From there, you can use transfer learning from a pre-trained CNN model like Inception (this is a helpful tutorial for how to do that), or train your own binary classifier with your tagged images, where your inputs are frames of traffic camera video.
I'd suggest that after learning the basics of CNNs with these models, only then should you dive into a more complicated model like yolo.
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.
I am building a new tensorflow model based off of SSD V1 coco model in order to perform real time object detection in a video but i m trying to find if there is a way to build a model where I can add a new class to the existing model so that my model has all those 90 classes available in SSD MOBILENET COCO v1 model and also contains the new classes that i want to classify.
For example, I have created training data for two classes: man, woman
Now, I built a new tensorflow model that identifies a man and/or woman in a video. However, my model does not have the other 90 classes present in original SSD Mobilenet model. I am looking for a way to concatenate both models or pass more than one model to my code to detect the objects.
If you have any questions or if I am not clear, please feel free to probe me further.
The only way i find is you need to get dataset of SSD Mobilenet model on which it was trained.
Make sure all the images are present in one directory and annotations in another directory.
We should have a corresponding annotation file for each image file
ex: myimage.jpg and myimage.xml
If all the images of your customed dataset are of same formate with SSD Mobilenet model then annotate it with a tool called LabelImg.
Add that images and annotated files to respective images and annotations directory where we have already saved SSD Mobilenet.
Try regenerate new TFrecord and continue with remaining procedure on it.
You can use transfer learning with Tensorflow API.
Transfer learning allows you to load re-trained network and modify the fully connected layer by introducing your classes.
There is full description for this in the following references:
Codelab
A good explanation here
Tensorflow API here for more details
Also you can use google cloud platform for better and faster results:
I wish this helps you.
I don't think there is a way you can add your classes to the existing 90 classes without using the dataset it is previously trained with. Your only way is to use that dataset plus your own and retrain the model.
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