README.md

---
base_model: timm/resnet18d.ra2_in1k
base_model_relation: merge
datasets:
- DimitrisMantas/RoofSense
library_name: segmentation-models-pytorch
license: cc-by-4.0
metrics:
- accuracy
- confusion_matrix
- f1
- mean_iou
- precision
- recall
model-index:
- name: RoofSense
  results:
  - dataset:
      name: RoofSense
      type: DimitrisMantas/RoofSense
    metrics:
    - name: Average Accuracy
      type: accuracy
      value: 0.8499
    - name: Overall Accuracy
      type: accuracy
      value: 0.9113
    - name: Average Precision
      type: precision
      value: 0.842
    - name: mIoU
      type: mean_iou
      value: 0.7474
    task:
      name: Semantic Segmentation
      type: image-segmentation
pipeline_tag: image-segmentation
tags:
- aerial-imagery
- lidar
- data-fusion
- roofing-materials
- roofing-material-classification
- semantic-segmentation
---

---
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# Doc / guide: https://huggingface.co/docs/hub/model-cards
{{ card_data }}
---

# Model Card for RoofSense

<!-- Provide a quick summary of what the model is/does. -->

An encoder-decoder semantic segmentation model for multimodal roofing material classification.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

The model adopts an encoder-decoder architecture, pairing ResNet-18-D with DeepLabv3+.
Following hyperparameter optimisation, the encoder blocks were augmented with anti-aliasing and efficient channel attention modules.
In addition, the global average pooling blocks in the encoder were replaced with the mean of average and maximum pooling.
Furthermore, dilation rates of the atrous spatial pyramid pooling block of the decoder were set to $\left(20, 15, 6\right)$.
Finally, address any labelling errors and improve predicitions in small regions, the decorer output stride was set to sixteen.

- **Developed by:** Dimitris Mantas, Delft University of Technology, The Netherlands
- **Model type:** Fully Convolutional Neural Network
- **License:** Creative Commons Attribution 4.0 International
- **Base Model:** timm/resnet18d.ra2_in1k (Transfer Learning)

### Model Sources

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- **Repository:** https://github.com/DimitrisMantas/RoofSense
- **Resources:** https://repository.tudelft.nl/record/uuid:c463e920-61e6-40c5-89e9-25354fadf549

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## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary

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## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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## Technical Specifications [optional]

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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