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--- |
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license: other |
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base_model: nvidia/mit-b0 |
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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-b0-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-b0-finetuned-sudoku |
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This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the mrkprc1/SudokuBoundaries2 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.5465 |
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- Mean Iou: 0.2407 |
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- Mean Accuracy: 0.5 |
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- Overall Accuracy: 0.4814 |
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- Accuracy Unlabelled: 1.0 |
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- Accuracy Sudoku-boundary: 0.0 |
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- Iou Unlabelled: 0.4814 |
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- Iou Sudoku-boundary: 0.0 |
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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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| 0.6257 | 2.5 | 20 | 0.7024 | 0.2992 | 0.4856 | 0.4769 | 0.7186 | 0.2525 | 0.3981 | 0.2002 | |
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| 0.6194 | 5.0 | 40 | 0.7513 | 0.2593 | 0.4960 | 0.4797 | 0.9332 | 0.0588 | 0.4633 | 0.0553 | |
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| 0.6134 | 7.5 | 60 | 0.8649 | 0.2428 | 0.4993 | 0.4809 | 0.9921 | 0.0065 | 0.4792 | 0.0065 | |
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| 0.4962 | 10.0 | 80 | 0.9245 | 0.2434 | 0.5006 | 0.4822 | 0.9949 | 0.0063 | 0.4805 | 0.0063 | |
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| 0.5552 | 12.5 | 100 | 0.8606 | 0.2442 | 0.5009 | 0.4826 | 0.9939 | 0.0080 | 0.4804 | 0.0079 | |
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| 0.6282 | 15.0 | 120 | 1.1507 | 0.2407 | 0.5000 | 0.4814 | 1.0000 | 0.0000 | 0.4813 | 0.0000 | |
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| 0.4042 | 17.5 | 140 | 1.0916 | 0.2408 | 0.4997 | 0.4811 | 0.9988 | 0.0007 | 0.4810 | 0.0007 | |
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| 0.8174 | 20.0 | 160 | 0.9731 | 0.2424 | 0.4991 | 0.4807 | 0.9926 | 0.0056 | 0.4792 | 0.0055 | |
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| 0.5353 | 22.5 | 180 | 0.9754 | 0.2409 | 0.4991 | 0.4805 | 0.9964 | 0.0017 | 0.4801 | 0.0017 | |
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| 0.4792 | 25.0 | 200 | 1.6835 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 | |
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| 0.4244 | 27.5 | 220 | 1.5039 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 | |
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| 0.376 | 30.0 | 240 | 2.2746 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 | |
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| 0.4129 | 32.5 | 260 | 2.0116 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 | |
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| 0.4717 | 35.0 | 280 | 1.8957 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 | |
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| 0.4229 | 37.5 | 300 | 1.7574 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 | |
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| 0.5708 | 40.0 | 320 | 2.0764 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 | |
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| 0.5826 | 42.5 | 340 | 1.6177 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 | |
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| 0.3765 | 45.0 | 360 | 1.8119 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 | |
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| 0.3704 | 47.5 | 380 | 1.6863 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 | |
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| 1.3265 | 50.0 | 400 | 1.5465 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 | |
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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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