---
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