search

lhoyer / improving_segmentation_with_selfsupervised_depth

[CVPR21] Implementation of our work "Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation"
244 stars 30 forks source link

Training with custom dataset #1

Closed gruossomonica closed 3 years ago

gruossomonica commented 3 years ago

Hi, thank you for your interesting work. I'm interested in semi-supervised segmentation. I have a question. Can I perform training using your approach with my own dataset? I collected about 5k images with binary segmentation masks (foreground and background classes). What are the tips and steps in this case? I look forward to your reply. Thank you.

Best Regards,

Monica

lhoyer commented 3 years ago

Thank you for your interest in our work. To apply our method, your dataset has to fulfill the following criteria:

If that is the case, you can check out the class CityscapesLoader in loader/cityscapes_loader.py for a reference on how to implement your own data loader. It inherits from loader/sequence_segmentation_loader.py, where you can find out more about the underlying data loading logic. In order to adapt the framework to the new data loader, a search with the keyword "cityscapes" will reveal the relevant places. After that, the README.md will guide you through the training process.

gruossomonica commented 3 years ago

Thanks for the detailed explanation.

Best Regards,

Monica

AnukritiSinghh commented 3 years ago

Hey @gruossomonica, were you able to run EXP 210 or 211? how did you place your dataset in the folders? Thank you

gruossomonica commented 3 years ago

Hi @AnukritiSinghh! Sorry, I didn't try because I didn't have the information about the camera intrinsics and more than one camera was used to collect my dataset.

Best regards.

fjremnav commented 3 years ago

@gruossomonica

Have you succeeded to modify the codes for your own datasets?

Thanks,