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pipeline_tag: image-classification |
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--- |
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## Table cell classification |
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The model is trained to classify table cell images as either empty or not empty. It has been trained using |
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table cell images from Finnish census and death record tables from the 1930s. |
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The model has been trained using [densenet121](https://pytorch.org/vision/stable/models/generated/torchvision.models.densenet121.html) as the base model, |
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and it has been transformed into the [onnx](https://onnx.ai/) format. |
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## Intended uses & limitations |
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The model has been trained to classify table cells from specific kinds of tables, which contain mainly handwritten text. |
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It has not been tested with other type of table cell data. |
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## Training and validation data |
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Training dataset consisted of |
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- empty cell images: 2943 |
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- non-empty cell images: 5033 |
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Validation dataset consisted of |
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- empty cell images: 367 |
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- non-empty cell images: 627 |
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## Training procedure |
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The code used for model training is available in the repository in `train.py` file, which uses functions from |
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`augment.py` and `utils.py` files. The required libraries are listed in the `requirements.txt` file. |
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The model was trained using cpu with the following hyperparameters: |
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- image size: 2560 |
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- learning rate: 0.0001 |
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- train batch size: 32 |
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- epochs: 15 |
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- patience: 3 epochs |
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- optimizer: Adam |
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## Evaluation results |
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Evaluation results using the validation dataset are listed below: |
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|Validation loss|Validation accuracy|Validation F1-score |
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|:----|:----|:----| |
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0.0427|0.9899|0.9903 |
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## Inference |
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Inference can be performed using the code in the `test.py` file. |
