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--- |
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tags: |
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- autotrain |
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- vision |
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- image-classification |
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- lam |
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datasets: |
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- Livingwithmachines/MapReader_Data_SIGSPATIAL_2022 |
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widget: |
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- src: >- |
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https://huggingface.co/davanstrien/autotrain-mapreader-5000-40830105612/resolve/main/1.png |
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example_title: patch |
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- src: >- |
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https://huggingface.co/davanstrien/autotrain-mapreader-5000-40830105612/resolve/main/271.png |
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example_title: patch |
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co2_eq_emissions: |
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emissions: 0.008077657735064319 |
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pipeline_tag: image-classification |
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--- |
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# Model Trained Using AutoTrain |
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Image classification model trained to predict whether a patch of a historic map contains 'railspace' or not. See the [dataset](https://huggingface.co/datasets/Livingwithmachines/MapReader_Data_SIGSPATIAL_2022) used for training for more information on the labels. |
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- Problem type: Multi-class Classification |
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- Model ID: 40830105612 |
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- CO2 Emissions (in grams): 0.0081 |
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## Validation Metrics |
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- Loss: 0.038 |
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- Accuracy: 0.995 |
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- Macro F1: 0.983 |
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- Micro F1: 0.995 |
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- Weighted F1: 0.995 |
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- Macro Precision: 0.991 |
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- Micro Precision: 0.995 |
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- Weighted Precision: 0.995 |
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- Macro Recall: 0.975 |
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- Micro Recall: 0.995 |
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- Weighted Recall: 0.995 |
