README.md

---
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

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->


## Bias, Risks, and Limitations

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### Recommendations

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{{ bias_recommendations | default("Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", true)}}

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** {{ training_regime | default("[More Information Needed]", true)}} <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

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#### Metrics

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### Results

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#### Summary

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## Technical Specifications [optional]

### Model Architecture and Objective

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## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

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**APA:**

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## Contact
ai-challenge@ign.fr