4 Bands

[danjac94]

No description provided.

[ancientpi3]

Hey dj, sorry for the late reply. There is no great way to adapt Prithvi to accept 4 input channels without blowing away the existing architecture. One trick could be to broadcast your NIR to 3 channels (RGB + NIR + NIR + NIR) to meet that 6-channel requirement. In my experience, Prithvi is a bit difficult to use for those who are new to DL. Hence why I put together this guide for those who might be struggling to use it. I honestly suggest looking at torchgeo https://github.com/microsoft/torchgeo and fastai https://github.com/fastai/fastai for your geospatial deep learning project. If you can adapt your data to be 3-band then there is a great selection of pre-trained ImageNet models to use. Good luck dude!

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On Thu, May 23, 2024 at 5:03 AM dj1994 ***@***.***> wrote: I'm totally new in DL and RS. What im trying to do is to finetune Prithvi model for one specific task. The main idea is first, I will Fine-tune the model with my own dataset that consist in Satellite imagery (RGB+NIR). My question is if there is any problem by finetuning the model with a different type of data in this case from MS(6 Bands) from the original model to (4 Bands). Do you have any recommendation or suggestion for this? Thank you very much in advance — Reply to this email directly, view it on GitHub <#1>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ARW7HYWPJYSFPBIYL57SJUTZDWWFXAVCNFSM6AAAAABIFGSVCCVHI2DSMVQWIX3LMV43ASLTON2WKOZSGMYTEMZWGQYTQMQ> . You are receiving this because you are subscribed to this thread.Message ID: ***@***.***>