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Thanks for your work. I tried training it on the monocular endoscopy dataset EndoSALM, but the results were poor. #159
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Hi, I haven't worked on such dataset. What is the actual range of your ground-truth? Maybe 100 is too large for your dataset. |
Thanks for your reply. The actual range of my data set is 0-100mm. The unit of your code parameter is meter. How should I change it appropriately? |
If the range is 0-100mm, please make sure that your evaluation code can correctly deal with this range. |
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I have changed it there.I think it's a problem with the training effect. But I don't know what causes the poor training effect. |
If your camera intrinsic is correct... How are the curves and visualizations during training? Have you checked them using tensorboard? |
The visualization looks not bad. But your evaluation results are totally bad. I think the problem is still from your dataset. I saw several papers using Lite-Mono for endoscopy depth estimation and they reported good results. |
OK. I am using the public EndoSLAM dataset, so it should be fine. Maybe there is something wrong with the loader I use to process the data. Can you tell me the title of the paper? |
Thanks for your help. |
You are welcome. Please update if you find the reason. |
OK |
The color images of the dataset are 320*320, as follows:
The real depth images of the dataset are saved in 32-bit .png format, as follows:
The data loader I wrote is as follows:
My evaluation results were very bad, and the results are as follows:
What do you think is the reason? I changed max_depth to 100
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