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earthsworld t1_j77dxyv wrote

seriously? you have a team of people to work with and you're asking reddit instead of doing the assignment? isn't that sort of cheating?

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DingWrong t1_j77e86c wrote

Try asking ChatGPT. That's what the world is using for their assignments.

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errorr_unknown OP t1_j77fph2 wrote

I wouldn't have asked here if i haven't tried myself , ML is not our specialist also no , it's not considered cheating as I'm willing to build the system by myself asking on reddit is no difference than searching on Google

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jnfinity t1_j77g6g6 wrote

I think this isn’t too complex to tackle. I suggest though, you try yourself, trust me, it will be fun.

Be analytical. Try dissecting the problems into smaller tasks. And I give you one hint: since you’re evaluating images, I suggest too read up on computer vision. Maybe PyTorch Vision or OpenCV might be your friends here ;)

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errorr_unknown OP t1_j77h8bh wrote

I have dealt with computer vision tasks before and used OpencCV!! but this is different it's a pre-annotated dataset to train a model to estimate length ? It's not our specialty and the time is short ,we just need to get to the core of it ..we already done the search so if you can give more clarification??

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sloikalamos t1_j77jfl8 wrote

This sounds fishy tbh. First if all, I'm not sure why were you assigned to a task that you never had any experience with. If this is an exam/school task, there's no way you haven't had any lesson related to it. As well as, multiple students work on it? One of you should have at least some ideas how to approach it. If this is a work test, you either overshoot something or saying you could do something that you couldn't.

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Meaveready t1_j77ksdp wrote

It would be a great start to first share what you tried.

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errorr_unknown OP t1_j77kvsd wrote

I guarantee you there is nothing fishy about this Its just a robotics contest with bunch of tasks and points for each one We happened to finish almost every task but this one due to as i mentioned no prior experience in machine learning or dealing with models/datasets We are in juniors team that's explain the less experience Not to mention that we are in a third world country?

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hiro_ono t1_j7830om wrote

Use a CNN and change the output to a single unit with no activation. It’s a regression problem so train with mean squared error. Just find a tutorial for CNNs in tensorflow or PyTorch

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XecutionStyle t1_j78jsua wrote

Error drives learning:

If Error ∝ (Target - Output)

Then you start your network with random weights (so the Output is random and error is large). When you pass the error back through the network, the weights are adjusted proportional to the error. Over time, the weights will settle where (Target - Output) is as low as possible.

This concept is true for any situation: if you're working with image data, no matter what architecture is used to produce the Output, you still compare it with Target (or 'label', Length of Pagrus for your case), and pass the Error back through the network to improve it iteratively.

Try building the simplest neuron: 1 input -> 1 output and use backpropagation to train until convergence.

For your assignment you could use a CNN (but a simple Feed-forward network would work too as you're just outputting 1 value for total length, so it's really a regression task) to get the Output, and train its weights which are internally shared (the window you shift across the image) which are trained the same way. You compute the output, compare it with the actual length of the Pagrus fish (you passed in as input), get the Error and the method above to improve the Network for the task applies.

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