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A very clear and in-depth explanation is provided by the slow R-CNN paper by Author(Girshick et. al) on page 12: C. Bounding-box regression and I simply paste here for quick reading: Moreover, the author took inspiration from an earlier paper and talked about the difference in the two techniques is below: After which in Fast-RCNN paper which you ...


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Image Classification Models are commonly referred as a combination of feature extraction and classification sub-modules. Where the total model excluding last layer is called feature extractor, and the last layer is called classifier. Popular Image Classification Models are: Resnet, Xception, VGG, Inception, Densenet and Mobilenet. Object Detection Models ...


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The paper cited does not mention linear regression at all. What it does is using a neural network to predict continuous variables, and refers to that as regression. The regression that is defined (which is not linear at all), is just a CNN with convolutional layers, and fully connected layers, but in the last fully connected layer, it does not apply sigmoid ...


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Where exactly are the weights updated? Is it between the initial convNet and the RPN. All updateable weights are updated during backpropagation. I assume you were trying to ask when are the weights updated?, well according to the original paper there are 3 way to train Faster R-CNN, each has it's own when to update weight time: Alternating training aka 4-...


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The researchers who made Tracknet finally open-sourced their code and will do so with dataset as soon as they settle their copyright issues. Here are the links: Arxiv paper: Tracknet Arxiv paper Tracknet repo If the link is broken, let me know and I'll upload the zip and tar.gz along with Readme file to Dropbox. Here is a link to the resulting video ...


2

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 The RPN gets the input from backbone network(VGG, Resnet etc.) as feature maps. Here the RPN itself a CNN layer so it will handle different shaped anchors to FC layer. For the loss calculation, each ...


1

If you really do need to use a ML solution for this problem (as opposed to a strict computer vision solution), most (all?) frameworks will return a list of detections after running inference. I'll use Tensorflow and this object detection tutorial as an example. If you run the colab and select the text cell with the headline "Visualizing the results"...


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Region Proposal Network is a subcomponent of the Fast RCNN and Faster RCNN architectures. It proposes candidate boxes and scores whether there is an object in this regions. RPN loss and objectness loss must be losses of this predictions. Regressor loss is the loss of the prediction of bounding box coordinates, and classifier loss is the loss of prediction of ...


1

Let me try to explain what exactly stride means generally, then you'll be able to address your specific problem. Let's say we have an images of size 7x7. Let's take a kernel of size 3x3. When you slide the kernel over image with : 1. stride=1 2. stride=2 Essentially stride means how much gap you should leave between two kernel position while applying ...


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After asking around about this, it seems the third option is the standard: take the mean of each feature map, and create a 2048-element feature vector. The search term for this is global pooling. That's what we are talking about that is the terminology I was missing. Global average pooling is good b/c it reduces the dimensionality before classification. ...


1

I think you got most of it from the way you wrote your question. How does R-CNN and AlexNet compare? Are they used for the same purpose or R-CNN does more? They are different things. AlexNet is a CNN architecture, i.e a neural network with a specific set of layers. R-CNN is multistep method that does object localization and classification using a search ...


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Wow. It is common for medical images to be heavily regulated (have to be kept on a separate server, cannot be copied, monitored access, etc...), but your situation is even tougher! Anyway, here's a few thoughts: I don't think you can extract conv features, because a great deal of information (sometimes nearly everything) for reconstruction can be stored in ...


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I believe that the resulting accuracy of your model is due to a mix of reasons. Firstly, the data-set is really small, so you cannot expect to have an efficient model just by using your 200 images. Proposed Solution #1: Data Augmentation. Enlarge your data-set by processing your images, crop, rotate, etc Examples with code. As for labeling the bounding ...


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You usually feed three color layers. So add additional frames like colors so you have 9 "color" frames for the image. The neural network will then automatically estimate how to treat each "color" layer.


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Disclaimer: This is a question that is probably going to be flagged since it is too broad and answers will be mainly based on opinion. Is this effort worth it? That is subjective, what commercial/social use does this have? Is this kind of detection really relevant? What can you do with those detections and classifications? That is something only an ...


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If all objects are observed in the same distance and almost same angle, the relative height and width can be helpful features for recognizing objects with similar shape and different size. By this features different methods like GAN algorithms such as CoGAN and BiGAN may help you in this problem. It should be noticed that for recognizing the size of the ...


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My first thought would be not to full deep learning on this - It is hard to see but it looks like your regions are bound by vertical lines with many horizontal ones spanning those regions. You can try doing just simple canny filters to detect those lines (maybe with [opencv] - [https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/...


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At the core of it, if you look at the source code, RPN is just a convolution layer with the number of maps=number of anchors per location, in you case it's 9. As any convolution layer, it's connected to the previous layer (also convolution, with 256 maps) using a kernel, in your case 3x3. This is exactly what 'Generate 9 anchors for each sliding window on ...


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For each anchor you find an IoU with the object in the picture and set 1 if IoUexceeds the threshold and 0 if it is below a lower tjreshold(e.g. 0.3). If it's a hit a bbox offset is calculated, distance between prediction and true bbox. Hence there are two loss fumctions: object/bg and bbox regression


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