metadata

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

modelcard.md?plain=1

Doc / guide: https://huggingface.co/docs/hub/model-cards

Model Card for RoofSense

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

Model Details

Model Description

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

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

Uses

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Training Details

Training Data

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DimitrisMantas
/

RoofSense