I have applied VGG16 to predict a classification problem. But the result was not good as I expected. So I applied U-Net separately to segment the region of interest. Now I want to merge these two codes like this, - If the image can segment the area of interest then it will give an output as 1, and if it can not then it will give an output as 0.
I need help regarding this. If you have any idea how o solve this problem please let me know.
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I want to retrain the object detector Yolov4 to recognize figures of the board game Ticket to Ride.
While gathering pictures i was searching for an idea to reduce the amount of needed pictures.
I was wondering if more instances of an object/class in a picture means more "training per picture" which leads to "i need less pictures"
Is this correct? If not could you try to explain in simple terms?
On the roboflow page, they say that the YOLOv4 breaks detecting objects into two pieces:
regression to identify object positioning via bounding boxes;
classification to classify the objects into classes.
Regression (analysis) is - in short - a method of analysis that tries to find the data (images in your case) that is relevant. Classification - on the other hand - transforms the ‘interesting’ images from the previous step into a class (which is ’train piece’, ’tracks’, ’station’ or something else that is worth separating from the rest).
Now, to answer your question: “no, you need more pictures.” When taking more pictures, YOLOv4 is using more samples make / test a more accurate classification. Yet, you have to be careful what you want to classify. You do want the algorithm to extract a ’train’ class from an image, but not an ‘ocean’ class for example. To prevent this, make more (different) pictures of the classes you want to have!
I am studying this paper right now https://arxiv.org/pdf/2003.05863.pdf . And here is the code https://github.com/switchablenorms/DeepFashion_Try_On .
I faced a problem while training Clothes Warping Module. I tried about 42(!!!) times to train it tweaking hyperparameters and result is always the same - I get artifacts like in the picture below. enter image description here
Why do they appear? It looks as if I do not understand something crucial. Maybe the problem of artifacts in training GANs is covered somewhere.
I am struggling to implement the generalized Dice loss for Caffe as Python Layer, which calculates loss for sub-volumes. I am hoping to get some help here. Or at least, if there is any code, please share the link.
I have 5 labels (0: background and labels1:4 for objects). Since I am getting a patch from 3D data, some of the subvolumes only contain the background. How the dice loss should be calculated for this sub-volumes?
Why in this line of code for creating One-hot label, the author has separated the background voxels counting?
Do we calculate the volume overlap for the background voxels too?
I have an image(e.g. 60x60) with multiple items inside it. Items are in the shape of square boxes, with say 4x4 dimensions, and are randomly placed within the image. The boxes(items) themselves are created with random patterns, some random pixels switched on and others switched off. So, it could be the same box repeated twice(or more in case of more than 2 items) in the image or could be entirely different.
I'm looking to create a deep learning model that could take in the original image(60x60) and output all the patches in the image.
This is all I have for now, but I can definitely share more details as the discussion starts. I'd be interested to weigh in different options that can help me achieve this objective. Thanks.
I would solve this using object detection. First I would train a network to detect those box like objects by cutting out patches of those objects. Then I would run a Faster R-CNN or something like this on it.
You might want to take a look at the stanford lecture on detection (slides here: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture11.pdf).
Suppose I have a CNN which is trained for classifying images of different animals, the output of such model will be a point (output point) in a n spatial dimension, where n is the number of animal classes the model is trained on; then that output is transformed in a way to convert it into a one-hot vector of n parameters, giving then the correct label for the image from the point of view of the CNN, but let's stick with the n dimensional point, which is the concept of an input image.
Suppose then that I want to take that point and transform it in a way so that the final output is an image with constraint width and height (the dimensions should be the same with different input images) which outputs the same point as the input image's, how do I do that?
I'm basically asking for the methods used (training mostly) for this kind of task, where an image must be reconstructed based on the output point of the CNN -I know the image will never be identical, but I'm looking for images that generate the same (or at least not so different) output point as a input image when that point is inputted to the CNN-. Take in mind that the input of the model I'm asking for is n and the output is a two (or three if it's not in grayscale) dimensional tensor. I noticed that deepdream does exactly this kind of thing (I think), but every time I put "deepdream" and "generate" in Google, an online generator is almost always shown, not the actual techniques; so if there are some answers to this I'd love to hear about them.
The output label does not contain enough information to reconstruct an entire image.
Quoting from the DeepDream example ipython notebook:
Making the "dream" images is very simple. Essentially it is just a gradient ascent process that tries to maximize the L2 norm of activations of a particular DNN layer.
So the algorithm modifies an existing image such that outputs of certain nodes in the network (can be in an intermediate layer, not necessarily the output nodes) become large. In order to do that, it has to calculate the gradient of the node output with respect to the input pixels.