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