---
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-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-b0-finetuned-sudoku

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the mrkprc1/SudokuBoundaries2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5465
- Mean Iou: 0.2407
- Mean Accuracy: 0.5
- Overall Accuracy: 0.4814
- Accuracy Unlabelled: 1.0
- Accuracy Sudoku-boundary: 0.0
- Iou Unlabelled: 0.4814
- Iou Sudoku-boundary: 0.0

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:------------------------:|:--------------:|:-------------------:|
| 0.6257        | 2.5   | 20   | 0.7024          | 0.2992   | 0.4856        | 0.4769           | 0.7186              | 0.2525                   | 0.3981         | 0.2002              |
| 0.6194        | 5.0   | 40   | 0.7513          | 0.2593   | 0.4960        | 0.4797           | 0.9332              | 0.0588                   | 0.4633         | 0.0553              |
| 0.6134        | 7.5   | 60   | 0.8649          | 0.2428   | 0.4993        | 0.4809           | 0.9921              | 0.0065                   | 0.4792         | 0.0065              |
| 0.4962        | 10.0  | 80   | 0.9245          | 0.2434   | 0.5006        | 0.4822           | 0.9949              | 0.0063                   | 0.4805         | 0.0063              |
| 0.5552        | 12.5  | 100  | 0.8606          | 0.2442   | 0.5009        | 0.4826           | 0.9939              | 0.0080                   | 0.4804         | 0.0079              |
| 0.6282        | 15.0  | 120  | 1.1507          | 0.2407   | 0.5000        | 0.4814           | 1.0000              | 0.0000                   | 0.4813         | 0.0000              |
| 0.4042        | 17.5  | 140  | 1.0916          | 0.2408   | 0.4997        | 0.4811           | 0.9988              | 0.0007                   | 0.4810         | 0.0007              |
| 0.8174        | 20.0  | 160  | 0.9731          | 0.2424   | 0.4991        | 0.4807           | 0.9926              | 0.0056                   | 0.4792         | 0.0055              |
| 0.5353        | 22.5  | 180  | 0.9754          | 0.2409   | 0.4991        | 0.4805           | 0.9964              | 0.0017                   | 0.4801         | 0.0017              |
| 0.4792        | 25.0  | 200  | 1.6835          | 0.2407   | 0.5           | 0.4814           | 1.0                 | 0.0                      | 0.4814         | 0.0                 |
| 0.4244        | 27.5  | 220  | 1.5039          | 0.2407   | 0.5           | 0.4814           | 1.0                 | 0.0                      | 0.4814         | 0.0                 |
| 0.376         | 30.0  | 240  | 2.2746          | 0.2407   | 0.5           | 0.4814           | 1.0                 | 0.0                      | 0.4814         | 0.0                 |
| 0.4129        | 32.5  | 260  | 2.0116          | 0.2407   | 0.5           | 0.4814           | 1.0                 | 0.0                      | 0.4814         | 0.0                 |
| 0.4717        | 35.0  | 280  | 1.8957          | 0.2407   | 0.5           | 0.4814           | 1.0                 | 0.0                      | 0.4814         | 0.0                 |
| 0.4229        | 37.5  | 300  | 1.7574          | 0.2407   | 0.5           | 0.4814           | 1.0                 | 0.0                      | 0.4814         | 0.0                 |
| 0.5708        | 40.0  | 320  | 2.0764          | 0.2407   | 0.5           | 0.4814           | 1.0                 | 0.0                      | 0.4814         | 0.0                 |
| 0.5826        | 42.5  | 340  | 1.6177          | 0.2407   | 0.5           | 0.4814           | 1.0                 | 0.0                      | 0.4814         | 0.0                 |
| 0.3765        | 45.0  | 360  | 1.8119          | 0.2407   | 0.5           | 0.4814           | 1.0                 | 0.0                      | 0.4814         | 0.0                 |
| 0.3704        | 47.5  | 380  | 1.6863          | 0.2407   | 0.5           | 0.4814           | 1.0                 | 0.0                      | 0.4814         | 0.0                 |
| 1.3265        | 50.0  | 400  | 1.5465          | 0.2407   | 0.5           | 0.4814           | 1.0                 | 0.0                      | 0.4814         | 0.0                 |


### Framework versions

- Transformers 4.37.1
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1