Comments (1)
hi ,HeeseongEom!
I'm so sorry that i don't know much about the Prompt module.And i also dont understand whats your mean about point prompts
.Do you mean a new method of prompts?
In the paper,the author says that the Prompt method he uses is based on mask random generation. Using the initially obtained segmentation mask to keep iterating as a new Prompt. that's why during the prediction process, you will get four different prediction files (of course, this is my understanding, due to time constraints, I didn't read through this code, so this is just my guess)
At the same time, I personally don't think that this Prompt method is in line with what SAM was originally meant to be, but it does seem like a more reasonable way to automate the training, otherwise you would need to keep clicking on the points yourself to generate the prompts during the training process.
If you mean to utilize a your own prompt method, you can try the results for comparing, I don't have too much to suggest for that.
Sorry again that nothing can help you. Good luck and have a good day.:)
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Related Issues (20)
- How to install required libraries? HOT 1
- ResizeLongestSide3D not used
- multi label mask HOT 1
- Does SAM-MED3D support automatic segmentation?
- 【train】【performance】about the fine tune result
- About inference.py
- Sliding Window Issue Aggregation Issue HOT 2
- could you tell me if I need to preprocess the images,Thank you sir?
- Data structuring is inefficient?
- Errors that occur during training(训练出错) HOT 3
- How are BRATS sequences used?
- Possible bias in training
- How to use box prompt?
- Training procedure
- inference 结果有bug HOT 2
- one-hot-labels HOT 1
- All values found in the mask "label" are zero.
- ValueError:high<=0 && points=[]
- infer_sequence.py with our click points
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