There are many small inaccuracies in the segmentation results on my dataset. Could you please provide some suggestions?

[clawCa]

Thank you for open sourcing your code! I trained the model on my own dataset. In my segmentation scenario, I have a class representing over 30 potted plants. the average precision (AP) value for this class is an impressive 0.9. However, upon reviewing the segmentation results, I noticed the presence of many small instances resembling speckles on each potted plant. Could you provide some tuning advice?

[narges-tk]

Thank you for open sourcing your code! I trained the model on my own dataset. In my segmentation scenario, I have a class representing over 30 potted plants. the average precision (AP) value for this class is an impressive 0.9. However, upon reviewing the segmentation results, I noticed the presence of many small instances resembling speckles on each potted plant. Could you provide some tuning advice?

Hi! May I please ask you to share the versions of the modules that are working for you? I am getting the error especially for hydra. It seems that the reported versions in environments no longer work. Thanks in advance for your help!

[clawCa]

Hi! May I please ask you to share the versions of the modules that are working for you? I am getting the error especially for hydra. It seems that the reported versions in environments no longer work. Thanks in advance for your help!

hydra-core == 1.0.5, maybe you can try offline mode. environment_mask3d.zip

[narges-tk]

Hi! May I please ask you to share the versions of the modules that are working for you? I am getting the error especially for hydra. It seems that the reported versions in environments no longer work. Thanks in advance for your help!

hydra-core == 1.0.5, maybe you can try offline mode. environment_mask3d.zip

Many thanks for your great help!

[narges-tk]

Thank you for open sourcing your code! I trained the model on my own dataset. In my segmentation scenario, I have a class representing over 30 potted plants. the average precision (AP) value for this class is an impressive 0.9. However, upon reviewing the segmentation results, I noticed the presence of many small instances resembling speckles on each potted plant. Could you provide some tuning advice?

Hi! May I please ask to guide me through how to visualize segmentation results? In fact, the following figure is the content of my visualization folder. However, when I open the .bin files of segmentation in cloud compare it is a huge file for even a very small subset and can not be opened. Thanks in advance for your help.

[clawCa]

Thank you for open sourcing your code! I trained the model on my own dataset. In my segmentation scenario, I have a class representing over 30 potted plants. the average precision (AP) value for this class is an impressive 0.9. However, upon reviewing the segmentation results, I noticed the presence of many small instances resembling speckles on each potted plant. Could you provide some tuning advice?

Hi! May I please ask to guide me through how to visualize segmentation results? In fact, the following figure is the content of my visualization folder. However, when I open the .bin files of segmentation in cloud compare it is a huge file for even a very small subset and can not be opened. Thanks in advance for your help.

Based on the generated bin file names, find the code part that stores the point cloud. You need to add the code to save the point cloud and its labels. In another issue, the author replied about the methods for visualization.

[narges-tk]

Thank you for open sourcing your code! I trained the model on my own dataset. In my segmentation scenario, I have a class representing over 30 potted plants. the average precision (AP) value for this class is an impressive 0.9. However, upon reviewing the segmentation results, I noticed the presence of many small instances resembling speckles on each potted plant. Could you provide some tuning advice?

Hi! May I please ask to guide me through how to visualize segmentation results? In fact, the following figure is the content of my visualization folder. However, when I open the .bin files of segmentation in cloud compare it is a huge file for even a very small subset and can not be opened. Thanks in advance for your help.

Based on the generated bin file names, find the code part that stores the point cloud. You need to add the code to save the point cloud and its labels. In another issue, the author replied about the methods for visualization.

Thanks a lot for your prompt help!

[bh-cai]

Thank you for your issues,may I know how your dataset is made, and how to use it?

[bh-cai]

Thank you for open sourcing your code! I trained the model on my own dataset. In my segmentation scenario, I have a class representing over 30 potted plants. the average precision (AP) value for this class is an impressive 0.9. However, upon reviewing the segmentation results, I noticed the presence of many small instances resembling speckles on each potted plant. Could you provide some tuning advice?

Thank you for your issues,may I know how your dataset is made, and how to use it?

[clawCa]

Thank you for your issues,may I know how your dataset is made, and how to use it?

You need to modify the pre-processing code provided by the author based on the format of your own dataset.

[Lizhinwafu]

What's your training code and testing code?

[BruceWayne233]

Thank you for your issues,may I know how your dataset is made, and how to use it?

You need to modify the pre-processing code provided by the author based on the format of your own dataset.

I tried to change the dataset to my own, but the predicted semantic label is always 1. And the number of points in the saved ply data is always larger than the original one. Have you ever encountered this situation? I hope to get your response in your busy schedule. Thank you.

[clawCa]

我尝试将数据集更改为我自己的数据集，但预测的语义标签始终为1。并且保存的层数据中的点数始终大于原始数据集。您遇到过这种情况吗？希望在您百忙之中得到您的回复。谢谢。

Yes, I've also encountered the issue of having more points than the original. My predicted semantics are all correct, it might be due to parameter settings or issues with the dataset preprocessing program.