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suflaj t1_iuji61k wrote

Reply to comment by Rare_Lingonberry289 in Does the length/size of a dimension affect accuracy? (CNN) by Rare_Lingonberry289

Probably not.

  • I am almost certain you don't have data that would take advantage of this dimensionality or the resources to process it
  • you can't accumulate so many features and remember all of them in recurrent models
  • I am almost certain you don't have the hardware to house such a large transformer model that could process it
  • I am almost certain you will not get a 365 day history of a sample during inference, 4 days seems more reasonable
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Rare_Lingonberry289 OP t1_iujpxs1 wrote

Ok so what if I have something like 10 temporal points, assuming you I have the necessary computing power. The other dimensions are pixels (x and y) and channels (bands).

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suflaj t1_iuk2gfj wrote

Yeah, just experiment with it. Like I said, I would start with 4. Then go higher or lower depending on your needs. I have personally not seen a temporally sensitive neural network to go beyond 6 or 8 time points. As with anything, there are tradeoffs.

Although if you have x, y and c, you will be doing 3D convolutions, not 4D. A 4D convolution on 4D data is essentially a linear layer.

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Rare_Lingonberry289 OP t1_iuk3uxk wrote

Ok, that makes sense. One more thing though. According to my research, temporal points during the spring and autumn are more helpful for what I'm trying to do. However, I'm afraid that large jumps like this will confuse my model. Like it will have a hard time detecting features when time jumps like this happen. Is this a real concern?

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suflaj t1_iuk9d9w wrote

As long as you keep the jumps the same it should be fine.

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