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Hi,

When I try to load the classifier saved after the training step I get this error,

\opencv\modules\core\src\alloc.cpp:55: error: (-4) Failed to allocate 262144 bytes in function cv::OutOfMemoryError

Is there anything I can do/change to solve this?

My input point cloud is a fairly small (but dense) point cloud from the SNCF,
https://ressources.data.sncf.com/explor ... formation/

My computer has 32GB of RAM.

This is how my classifier.txt file looks like,
# 3DMASC classifier file
classifier: classifier.yaml
# Clouds (roles)
cloud: PC1
# Core points (classified role)
core_points: PC1
# Features
feature: Dip_SC0.5_PC1
feature: Dip_SC1_PC1
feature: Dip_SC1.5_PC1
feature: Dip_SC2_PC1
feature: DipDir_SC0.5_PC1
feature: DipDir_SC1_PC1
feature: DipDir_SC1.5_PC1
feature: DipDir_SC2_PC1
feature: PCA1_SC0.5_PC1
feature: PCA1_SC1_PC1
feature: PCA1_SC1.5_PC1
feature: PCA1_SC2_PC1
feature: PCA2_SC0.5_PC1
feature: PCA2_SC1_PC1
feature: PCA2_SC1.5_PC1
feature: PCA2_SC2_PC1
feature: PCA3_SC0.5_PC1
feature: PCA3_SC1_PC1
feature: PCA3_SC1.5_PC1
feature: PCA3_SC2_PC1
feature: SPHER_SC0.5_PC1
feature: SPHER_SC1_PC1
feature: SPHER_SC1.5_PC1
feature: SPHER_SC2_PC1
feature: ANISO_SC0.5_PC1
feature: ANISO_SC1_PC1
feature: ANISO_SC1.5_PC1
feature: ANISO_SC2_PC1
feature: LINEA_SC0.5_PC1
feature: LINEA_SC1_PC1
feature: LINEA_SC1.5_PC1
feature: LINEA_SC2_PC1
feature: PLANA_SC0.5_PC1
feature: PLANA_SC1_PC1
feature: PLANA_SC1.5_PC1
feature: PLANA_SC2_PC1
feature: Zmin_SC0.5_PC1
feature: Zmin_SC1_PC1
feature: Zmin_SC1.5_PC1
feature: Zmin_SC2_PC1
feature: Zmax_SC0.5_PC1
feature: Zmax_SC1_PC1
feature: Zmax_SC1.5_PC1
feature: Zmax_SC2_PC1
feature: ZRANGE_SC0.5_PC1
feature: ZRANGE_SC1_PC1
feature: ZRANGE_SC1.5_PC1
feature: ZRANGE_SC2_PC1

Please provide the classifier.yaml file for testing.
Which laz did you use among the SNCF dataset? There are 16 files available.

Thank you for the reply!

I used the railway_000033.laz file.

The classifier.yaml file is large (8,53 GB (9 160 938 366 bytes)). I will try to find a way to share it.
Is the size of the .yaml file as expected?

Your yaml seems quite large.
What were your parameters in the training Dialog?
max depth
max tree count
active var count
min sample count

I used the following settings,

Max depth: 25
Max tree count: 100
Active var count: 0
Min sample count: 10

Scales: 0.5:0.5:2

Please provide the initial parameter file.
Did you launch the training on the overall dataset at once?

I launched the training on the railway_000033.laz file at once, without using a subset as core_points.

This is the initial parameter file,

# NAMES OF YOUR POINT CLOUDS
#cloud: PC1=railway_000033_rail.laz # Point cloud for training
cloud: PC1=
#cloud: corepoints=railway_000033_rail_sub04.laz

# CORE POINTS
#core_points: corepoints
core_points: PC1

# SCALES
scales: 0.5;1;1.5;2 # scales as a list

# FEATURES


feature: DIP_SCx_PC1
feature: DIPDIR_SCx_PC1
feature: PCA1_SCx_PC1
feature: PCA2_SCx_PC1
feature: PCA3_SCx_PC1
feature: SPHER_SCx_PC1
feature: ANISO_SCx_PC1
feature: LINEA_SCx_PC1
feature: PLANA_SCx_PC1
feature: Zmin_SCx_PC1
feature: Zmax_SCx_PC1
feature: ZRANGE_SCx_PC1
#feature: NORMDIP_SCx_MEDIAN_PC1

#We already calculated the verticality, you can ignore it if an error shows
#feature: VERT_SCx_MEDIAN_PC1

I launched a training with the same parameter file and the same Random Trees dialog parameters. The classifier is quite large, about 700Mo, but not as much as yours. I can load it after to classify other points. Are you sure about your configuration?

Ok, thank you very much. I really appreciate your help!

Regarding this,
Are you sure about your configuration?

Could you clarify? Do you mean if I installed CC and its dependencies correctly, or do you mean the 3DMASC settings/configuration?

Anyway, I will give it another try. Sounds like it would be useful to know if I can reproduce my issue.

By configuration I meant 3DMASC settings. Yes, being able to reproduce the issue is mandatory.