Create README.md

README.md

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