metadata
license: etalab-2.0
tags:
- pytorch
- segmentation
- point clouds
- aerial lidar scanning
- IGN
model-index:
- name: FRACTAL-LidarHD_7cl_randlanet
results:
- task:
type: semantic-segmentation
dataset:
name: IGNF/FRACTAL
type: point-cloud-segmentation-dataset
metrics:
- name: mIoU
type: mIoU
value: 77.2
- name: IoU Other
type: IoU
value: 48.1
- name: IoU Ground
type: IoU
value: 91.7
- name: IoU Vegetation
type: IoU
value: 93.7
- name: IoU Building
type: IoU
value: 90
- name: IoU Water
type: IoU
value: 90.8
- name: IoU Bridge
type: IoU
value: 63.5
- name: IoU Permanent Structure
type: IoU
value: 59.9
FRACTAL-LidarHD_7cl_randlanet
The general characteristics of this specific model FRACTAL-LidarHD_7cl_randlanet are :
- Trained with the FRACTAL dataset
- Aerial lidar point clouds, colorized with rgb + near-infrared, with high point density (~40 pts/m²)
- RandLa-Net architecture as implemented in the Myria3D library
- 7 class nomenclature : [other, ground, vegetation, building, water, bridge, permanent structure]
Model Informations
- Code repository: https://github.com/IGNF/myria3d (V3.8)
- Paper: TBD
- Developed by: IGN
- Compute infrastructure:
- software: python, pytorch-lightning
- hardware: in-house HPC/AI resources
- License: : Etalab 2.0
Uses
Bias, Risks, Limitations and Recommendations
How to Get Started with the Model
Visit (https://github.com/IGNF/FLAIR-1) to use the model. Fine-tuning and prediction tasks are detailed in the README file.
Training Details
Training Data
Training Procedure
Preprocessing
Training Hyperparameters
Speeds, Sizes, Times
Evaluation
Testing Data, Factors & Metrics
Testing Data
Metrics
Results
Samples of results
Citation
BibTeX:
APA: