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---
license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
base_model: nvidia/mit-b1
model-index:
- name: segformer-b1-finetuned-sudoku
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b1-finetuned-sudoku
This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the mrkprc1/SudokuBoundaries2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7703
- Mean Iou: 0.0967
- Mean Accuracy: 0.1934
- Overall Accuracy: 0.1934
- Accuracy Unlabelled: nan
- Accuracy Sudoku-boundary: 0.1934
- Iou Unlabelled: 0.0
- Iou Sudoku-boundary: 0.1934
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:------------------------:|:--------------:|:-------------------:|
| 0.6531 | 3.33 | 20 | 0.7016 | 0.1433 | 0.2867 | 0.2867 | nan | 0.2867 | 0.0 | 0.2867 |
| 0.7654 | 6.67 | 40 | 0.7142 | 0.3064 | 0.6129 | 0.6129 | nan | 0.6129 | 0.0 | 0.6129 |
| 0.4761 | 10.0 | 60 | 1.0391 | 0.0002 | 0.0005 | 0.0005 | nan | 0.0005 | 0.0 | 0.0005 |
| 0.7746 | 13.33 | 80 | 1.7648 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 |
| 0.5488 | 16.67 | 100 | 1.2288 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 |
| 0.6242 | 20.0 | 120 | 1.5012 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 |
| 0.5423 | 23.33 | 140 | 0.9650 | 0.0029 | 0.0059 | 0.0059 | nan | 0.0059 | 0.0 | 0.0059 |
| 0.521 | 26.67 | 160 | 0.8594 | 0.0197 | 0.0393 | 0.0393 | nan | 0.0393 | 0.0 | 0.0393 |
| 0.5655 | 30.0 | 180 | 0.7950 | 0.0527 | 0.1055 | 0.1055 | nan | 0.1055 | 0.0 | 0.1055 |
| 0.4229 | 33.33 | 200 | 0.7910 | 0.0982 | 0.1964 | 0.1964 | nan | 0.1964 | 0.0 | 0.1964 |
| 0.288 | 36.67 | 220 | 0.7591 | 0.1358 | 0.2715 | 0.2715 | nan | 0.2715 | 0.0 | 0.2715 |
| 0.2002 | 40.0 | 240 | 0.7395 | 0.2414 | 0.4828 | 0.4828 | nan | 0.4828 | 0.0 | 0.4828 |
| 0.6014 | 43.33 | 260 | 0.7405 | 0.2644 | 0.5289 | 0.5289 | nan | 0.5289 | 0.0 | 0.5289 |
| 0.4336 | 46.67 | 280 | 0.7423 | 0.1751 | 0.3502 | 0.3502 | nan | 0.3502 | 0.0 | 0.3502 |
| 0.565 | 50.0 | 300 | 0.7703 | 0.0967 | 0.1934 | 0.1934 | nan | 0.1934 | 0.0 | 0.1934 |
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1
|