---
license: etalab-2.0
tags:
- segmentation
- pytorch
- aerial imagery
- landcover
- IGN
model-index:
- name: FLAIR-INC_RVBIE_unetresnet34_15cl_norm
  results:
  - task:
      type: semantic-segmentation
    dataset:
      name: IGNF/FLAIR#1-TEST
      type: earth-observation-dataset
    metrics:
    - name: mIoU
      type: mIoU
      value: 54.72
    - name: Overall Accuracy
      type: OA
      value: 76.37
    - name: Fscore
      type: Fscore
      value: 67.60
    - name: Precision
      type: Precision
      value: 69.35
    - name: Recall
      type: Recall
      value: 67.65
      
    - name: IoU Buildings
      type: IoU
      value: 82.3
    - name: IoU Pervious surface
      type: IoU
      value: 53.24
    - name: IoU Impervious surface
      type: IoU
      value: 74.17
    - name: IoU Bare soil
      type: IoU
      value: 60.40
    - name: IoU Water
      type: IoU
      value: 87.59
    - name: IoU Coniferous
      type: IoU
      value: 46.35
    - name: IoU Deciduous
      type: IoU
      value: 67.45
    - name: IoU Brushwood
      type: IoU
      value: 30.23
    - name: IoU Vineyard
      type: IoU
      value: 82.93
    - name: IoU Herbaceous vegetation
      type: IoU
      value: 55.03      
    - name: IoU Agricultural land
      type: IoU
      value: 52.01
    - name: IoU Plowed land
      type: IoU
      value: 40.84   
    - name: IoU Swimming pool
      type: IoU
      value: 48.44     
    - name: IoU Greenhouse
      type: IoU
      value: 39.44
      
pipeline_tag: image-segmentation
---


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## Model Informations

<!-- Provide the basic links for the model. -->

- **Repository:** https://github.com/IGNF/FLAIR-1-AI-Challenge
- **Paper [optional]:** https://arxiv.org/pdf/2211.12979.pdf
- **Developed by:** IGN
- **Compute infrastructure:** 
    - software: python, pytorch-lightning
    - hardware: GENCI, XXX
- **License:** : Apache 2.0


  
## Uses

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## Bias, Risks, and Limitations

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### Recommendations

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## How to Get Started with the Model

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

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## Evaluation

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### Testing Data, Factors & Metrics

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#### Metrics

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### Results

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#### Summary

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## Citation [optional]

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## Contact
ai-challenge@ign.fr