• Hardware (x86_ Linux ubuntu jammy 22.04, RTX 3090)
• Network Type (Mask2Former)
• Training spec file(spec.txt (1.9 KB))
Can you help me to fine tune my Mask2former Instance segmentation model on a custom dataset
Dataset has 3880 - training samples and 970 - validation samples(The annotations are in coco format),
I have 5 classes (in that 2 classes have extremely more labels comparative to other 3 classes).
My Image file dimension are width: 2208 and height: 1242 in .png format
Can you just specify any online augmentation techniques such as flips , rotation, etc. which can be included in augmentation config to handle class imbalance, because i don’t see anything like that in the docs.
Below i share my training graphs for visualization can help out to get an accuracy upto 97 to 99%
3 posts - 2 participants