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bad segmentation #119
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from pip, 0.2.24 is working fine, something went wrong in 0.3.0. Hope this will be helpful. |
@nisanaryal-MTEG Thank you for testing out. |
@vietanhdev I just selected the model from the option, the models are auto downloaded aren't they?. If not then the problem might be because of local downloaded file with the same name from previous version. I created the onnx with my own code and matched the input and output after watching the documentation. I did not know that there was a separate repository for onnx, thanks for the hard work. |
@nisanaryal-MTEG There is an issue here: Labeling UI is not blocked while downloading the model. Please wait until the message "Downloading http..." disappears. |
For the MobileSAM, please note that it has been only trained on 1% of the SAM dataset. Maybe the generalization is not as good as the original models, causing bad results on special objects or domains (just my guess). |
@vietanhdev sorry for the confusion, the model downloaded properly last time, I just took the image to convey that I am using the latest version of model. I converted MobileSAM to onnx with my code and tested on 0.2.24, it is working fine. All the models are giving wrong output from 0.3.0. The surgical image from previous comment was from vit-l. I have not tried on another PC yet so this might be a local problem as well. I will get back to you after testing on another pc. |
Could you try following:
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same here. Results are really bad for v0.3.2 (no matter where I prompt the image using whatever models). Using v0.2.24 is fine |
@nisanaryal-MTEG @ydzhang12345
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@vietanhdev the problem is solved in the v0.3.3. Thanks for the hard work |
I just updated to 0.3.2 and there seems to be some issue. The segmentation mask is wrong for mobileSAM as well as Vit-b version.(gives almost all the images as output). I think this is due to some error during the latest update. Please review it.
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