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You can use tf.reshape() method (tensorflow doc) to reshape (2048) dimensional tensor to (100,300). Here's one way to do this: input1 = tf.reshape(input1, [100,300], name="reshaped_tensor") If you're not using TensorFlow but using Numpy, here's an implementation: input1 = np.array(input1) input1 = np.reshape(input1, (100,300)) Note: You might want to ...


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Precision is the number of true positives over the number of predicted positives(PP), and recall is the number of true positives(TP) over the number of actual positives(AP) you get. I used the initials just to make it easier ahead. A true positive is when you predict a car in a place and there is a car in that place. A predicted positive is every car you ...


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Neural networks can have a lot of different structures. CNNs can have a number of parameters that ranges from a few thousands to several millions. In general you aim to increase the number of filters and reduce the first 2 dimensions, as you go deeper in the network. So if you had Conv -> pool -> Conv -> pool -> ... , you could do for example ...


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Look at Google's Open Image Dataset @ https://storage.googleapis.com/openimages/web/index.html They provide image-level labels, object bounding boxes, object segmentation masks, and visual relationships. Here is the link for the traffic signs dataset.


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In most modern neural network frameworks, the update rules for training can be selectively applied to some parameters and not others. How to do that is dependent on the framework. Some will have the concept of "freezing" a layer, preventing parameters in it being updated. Keras does this for example. Others will do the opposite and expect you to provide a ...


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The paper you are citing is the paper that introduced the cascaded convolution neural network. In fact, in this paper, the authors say To realize 3DDFA, we propose to combine two achievements in recent years, namely, Cascaded Regression and the Convolutional Neural Network (CNN). This combination requires the introduction of a new input feature which ...


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In short: It is easy to quantify information, but it is not easy to quantify its usefulness I'm not sure how exactly you are looking to formalise your experiment, but it might be helpful to consider these points: There is no such thing as an absolute measure of information. The amount of information contained in some dataset is dependent on the underlying ...


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Simply said, there is no specific "meaning" to the features generated. They are simply features that are fitted through math and calculus, and nobody knows what they represent exactly, and will never knows. However we can run PCA (Principal Component Analysis) to see which feature is the most "important" of all, aka which feature affects the most in the ...


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CNN is used since, it is effectively an optimized use case for dealing with image data. CNN effectively automatically extracts features from an images. Other techniques are more likely to not take full advantage of the data. CNN is able to make full use of the data by also including information from adjacent pixels and downsample through layers. Paper ...


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You can use the dataset test set as "frames" of video. Test the images with your model and calculate the images per second of the result and that is the same as frames per second. However you should set the batch size to 1 as in the real world scenario. You should also display each image with teh corresponding boxes after inference and remove the accuracy ...


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