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2

This could be possible, providing you have the right dataset to train it on. The volume of a cup consist of width, height and depth. You can probably detect all three of those given the bounding box or the pixels of the cup. However detecting the dimensions of an object require a reference object, like a penny or your finger and you have to specify the ...


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I did an experiment, took a trained densenet121 and kept the bottom layers. I trained the FC layer to a softmax and then to a lambda layer that normalized the vector. I trained the network with imagenet to make the outputs the most far a away from (1,1,1,1,1...1) as possible, so I would get one hot vectors. I did, but the network trained to a single category ...


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I'm working on a similar problem. I'm using a 2D point cloud of an object, for example, X and Y coordinates for height, and with that more simple data set I will train a regression model (currently working on that). In my opinion, this approach with dissecting complex point cloud into cross sections that contain wanted dimension and feeding that to the model ...


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