-
Notifications
You must be signed in to change notification settings - Fork 9
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Inference problems #15
Comments
Hi, Thanks for your interest. We think that these represent two different settings. The setting you mentioned pertains to traditional semantic or instance segmentation, whereas in our paper, we adopt a promptable setting where the user specifies the class of interest. Due to the different settings, we divide the methods in our comparison into specialist models (with a traditional setting) and SAM-based models (with a promptable setting). We consistently maintain a promptable setting with all SAM-based models to ensure a fair comparison, and we explicitly clarify this in the paper: "Challenge IoU measures the IoU between the predicted and ground-truth masks for only the classes present in an image, whereas IoU is computed across all classes. In our class promptable segmentation setting with class prompts provided, Challenge IoU and IoU yield identical results." |
I can understand that you trained through a subset of 40 groups and found that a certain folder had the best training (named it 0) right? |
Folder 0 is not the folder with the best performance; it is the original data without any data augmentations. Please read this comment for more detailed explanations of what these folders of different versions represent. |
Hi, I read your inference function. I noticed you first gave the program the specific class before inference (e.g., predicting category 3 for image 1). Is this controversial because it reduces the possibility of misclassification? For example, if image 1 has only class 3 and class 7, it won't produce a predicted mask for class 2 (even though it cannot distinguish between classes 2 and 3, 7). In other words, the authors tell the model which classes the predicted image contains (based on the true labels). Yet most images contain only a few classes.
As far as I'm concerned, it's fair to predict all the classes of an image (e.g., from category 1 to 7) one by one. If it performs well, this model should not predict classes that the image does not contain.
Looking forward to your reply.
The text was updated successfully, but these errors were encountered: