segformer-b1-finetuned-sudoku
This model is a fine-tuned version of nvidia/mit-b1 on the mrkprc1/SudokuBoundaries2 dataset. It achieves the following results on the evaluation set:
- Loss: 0.9826
- Mean Iou: 0.2452
- Mean Accuracy: 0.4999
- Overall Accuracy: 0.4903
- Accuracy Unlabelled: 0.9996
- Accuracy Sudoku-boundary: 0.0001
- Iou Unlabelled: 0.4903
- Iou Sudoku-boundary: 0.0001
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabelled | Accuracy Sudoku-boundary | Iou Unlabelled | Iou Sudoku-boundary |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.6034 | 3.33 | 20 | 0.6951 | 0.3427 | 0.5173 | 0.5149 | 0.6432 | 0.3914 | 0.3940 | 0.2913 |
| 0.7796 | 6.67 | 40 | 0.7150 | 0.3049 | 0.5083 | 0.5021 | 0.8309 | 0.1857 | 0.4501 | 0.1597 |
| 0.4378 | 10.0 | 60 | 0.9772 | 0.2452 | 0.5 | 0.4904 | 1.0 | 0.0 | 0.4904 | 0.0 |
| 0.6804 | 13.33 | 80 | 1.1605 | 0.2452 | 0.5 | 0.4904 | 1.0 | 0.0 | 0.4904 | 0.0 |
| 0.58 | 16.67 | 100 | 0.9787 | 0.2452 | 0.5 | 0.4904 | 1.0 | 0.0 | 0.4904 | 0.0 |
| 0.6563 | 20.0 | 120 | 1.1860 | 0.2452 | 0.5 | 0.4904 | 1.0 | 0.0 | 0.4904 | 0.0 |
| 0.5128 | 23.33 | 140 | 0.8884 | 0.2457 | 0.5002 | 0.4907 | 0.9996 | 0.0009 | 0.4905 | 0.0009 |
| 0.5054 | 26.67 | 160 | 0.8746 | 0.2455 | 0.5002 | 0.4907 | 0.9998 | 0.0006 | 0.4905 | 0.0006 |
| 0.5532 | 30.0 | 180 | 0.9540 | 0.2452 | 0.5000 | 0.4905 | 1.0 | 0.0000 | 0.4905 | 0.0000 |
| 0.3238 | 33.33 | 200 | 0.8916 | 0.2470 | 0.5009 | 0.4914 | 0.9984 | 0.0035 | 0.4905 | 0.0035 |
| 0.2964 | 36.67 | 220 | 1.0162 | 0.2453 | 0.5000 | 0.4905 | 1.0000 | 0.0000 | 0.4905 | 0.0000 |
| 0.2102 | 40.0 | 240 | 0.9650 | 0.2452 | 0.4998 | 0.4903 | 0.9996 | 0.0001 | 0.4903 | 0.0001 |
| 0.623 | 43.33 | 260 | 0.9071 | 0.2461 | 0.5004 | 0.4909 | 0.9991 | 0.0017 | 0.4904 | 0.0017 |
| 0.3741 | 46.67 | 280 | 0.9245 | 0.2454 | 0.5000 | 0.4904 | 0.9994 | 0.0006 | 0.4903 | 0.0006 |
| 0.5765 | 50.0 | 300 | 0.9826 | 0.2452 | 0.4999 | 0.4903 | 0.9996 | 0.0001 | 0.4903 | 0.0001 |
Framework versions
- Transformers 4.37.1
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1
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