SMP has a lot of different choices for architecture other than unet, and a ton of different encoders. I like deeplabv3+/unet with regnety encoder, works well for most things https://github.com/qubvel/segmentation_models.pytorch

That same page has an object detection demo, between the object detection and semantic segmentation examples there is probably enough info to figure out how to swap to instance seg

This is semantic segmentation but could be good starting point https://github.com/pytorch/android-demo-app/tree/master/ImageSegmentation

All you need to know is git clone

I have a msi ge66 raider my work got me, it’s 32gb ram rtx3070 8gb, 1TB SSD. I use it for PyTorch and when I max it out I just use runpod or lambda labs to rent a rtx3090 or a100 for dirt cheap. Would recommend it

Lmaooo

Laptops are great for prototyping, the one you mentioned will be fine. If you start to work with large models and max it out then just use cloud computing for those large train jobs

My best guess is coding mistake on your part. Good luck tivo

Well, clearly it’s not getting any better with what you’re trying. Maybe time to rethink

Data augmentation, dropout?

$2700 on Newegg my work got it for me. I have it on a stand, it definitely gets a little warm when running PyTorch but never been an issue. There is a key to turn the fans up to 100% instantly if needed but I’ve never had to use it.

https://www.newegg.com/titanium-blue-msi-ge-series-ge66-raider-11ug-271-gaming-entertainment/p/N82E16834155921

I have a msi laptop with a rtx3070 and 32gb of ram. It is plenty for experimenting and training small models/datasets. Once I have something working and need more power I rent gpus by the hour from lambda labs or run pod. For $1-2/hr I’m able to get around 80gb of gpu power. Long story short I would go for the cheaper and use the cloud when needed