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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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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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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 ...


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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. ...


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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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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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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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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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