There is a tutorial on the web for drawing bounding boxes using R-CCN, where a VGG16 network is modified for this task (using transfer learning take advantage that the inner layers are trained already.).
The edit consists on:
removing the classification layer
using a regression layer instead
The training involves images for inputs and [x1,y1,x2,y2] labeled outputs, each pair being a corner of an image, i.e a description of a square box around the object we want to detect.
I have tried it, and so far didn't have luck for the coordinates predicted. So my questions are:
Is the procedure of editing the CNN to create an R-CNN that outputs the vector (also in link at the top) a correct approach for predicting a bounding box for a specific object ?
I am trying with Mobile Net because it is lighter, so assuming 1. is correct, would this also be a "logically similar" idea?
Related
I am training a U-Net using Monai, which is based on Pytorch. I am using Decathlon Dataset, where each segmentation image has two labels (one for the organ and the other for the tumour). What I want is to ignore the first label (organ segmentation) and train the network on the second label (tumour segmentation). I don't know if I should delete one label from the images manually(This will take me a lot of time if I have hundreds of images). Is there a way to do it using code? What is the right way to do it? Is there any existing function in Monai? Because Opening each image as a tensor, reading its values and replacing label 1 with the background pixel might be time and resource-consuming. Thanks
I tried to search on Monai docs to get a simple code but I didn't find.
I am trying to understand RPN network in Faster RCNN.
I understand the concept of RPN network,
Pass the input images to the pre trained CNN, and get the output as feature maps
Make fixed size of the feature maps
Extract anchors (3 different scales and ratio for every sliding window) from the fixed size feature maps.
Use two 1×1 Fully connected NN to find the background or object and the bounding box coordinates (4 values)
Calculate IOU for Anchors bounding box with Ground Truth bounding box, if IOU>0.7, then the anchor has object, otherwise, the anchor has background.
The theme for RPN is to give the region proposals which have objects.
But, I do not understand the input and the output structure.
For example, I have 50 images, each images having 5 to 6 objects, and labeling informations(coordinates of each objects).
How do I generate target values, to train PRN Network...
In all the blogs, they shows the architecture as feed the entire image to the pre trained CNN.
And, the output of RPN, the model has to tell whether the anchor has object or not, and also predict the bounding box for the object in the anchor.
For this, how to prepare the input and target/output values like we do in dog/cat or dog/cat/car classification problem.
Let me correct if I am not correct,
Is that, we have to crop all the objects in every image and do binary classification as object vs background for classifying the anchor has object or not
And, Is that, we have to give the ground truth value as target for every cropped objects from all images in the dataset, so that the RPN network trained well to predict the bounding box for the object in every anchor.
Hope, I clearly explained my doubts.
Help me to learn this concept, Thank you
I'm working on a project that needs me to use keras to predict bounding box coordinates (object detection). I was brainstorming and want to try if it is possible to modify/use keras's ImageDataGenerator and .fit_generator() to predict bounding box?
I am seeking online documentation to help me figure out. So far I have not found any links that takes in an input of bounding boxes in .fit_generator().
I am learning object detection using R-CNN...
I have the images and the annotation file which gives the bounding box for the object
I understand these steps in R-CNN,
Using selective search to get the proposed regions
Make all the region same size
Feed those images in CNN
Save the feature maps and feed to SVM for classification
In training, I took all the objects (only the objects from images not the background) and feed to the CNN and then train the feature maps in SVM for classification.
In every blogs, all are saying in R-CNN, there are three parts,
1st -selective search
2nd -CNN
3rd -BBox Regression
But, I don't get the deep explanation of the BBox Regression.
I understand the IOU(Intercept over Union) to check the BBox accuracy.
Could you please help me to learn how this BBox Regression is used to get the coordinates of the object.
To explain about the BBox regression working which is as mentioned below.
Like you mentioned it happens in multiple stages.
Selective Search:
Generate initial sub-segmentation, we generate many candidates or part regions.
Use greedy algorithm to recursively combine similar regions into larger ones.
Use the generated regions to produce the final candidate region proposals.
CNN and BBox Regression:
The regressor is a CNN with convolutional layers, and fully connected layers, but in the last fully connected layer, it does not apply sigmoid or softmax, which is what is typically used in classification, as the values correspond to probabilities. Instead, what this CNN outputs are four values (𝑟,𝑐,ℎ,𝑤), where (𝑟,𝑐) specify the values of the position of the left corner and (ℎ,𝑤) the height and width of the window. In order to train this NN, the loss function will penalize when the outputs of the NN are very different from the labelled (𝑟,𝑐,ℎ,𝑤) in the training set.
![sample training input]http://www.cs.toronto.edu/~vmnih/data/mass_roads/train/sat/10078660_15.tiff
![sample training output]
http://www.cs.toronto.edu/~vmnih/data/mass_roads/train/map/10078660_15.tif
I am a beginner to CNN , and have worked with the MNIST dataset in which we input 28x28x3 images and output a 10x1 vector containing probabilities of the 10 classes(0,1,2,3---,9).
How do we extract only the road pixels from the input image and display them, as is represented by the output image?
This problems is a binary segmentation problem. In a sense you learn a mapping from satellite images and predict for each pixel, iff this pixel is part of the road. A simple algorithm to do this would be to check if the pixel color is part of some range.
A CNN naturally will learn you a more complicated function based on the local neighborhood of said pixel. One repo to get you started should be this one: https://github.com/jocicmarko/ultrasound-nerve-segmentation. Therein they use a similar algorithm to segment ultrasound images using CNNs. You just have to use 3 input channels instead of 1 and everything else should be quite similar.