---
license: other
base_model: nvidia/mit-b1
tags:
- vision
- image-segmentation
- generated_from_trainer
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: 0.9826
- Mean Iou: 0.2452
- Mean Accuracy: 0.4999
- Overall Accuracy: 0.4903
- Accuracy Unlabelled: 0.9996
- Accuracy Sudoku-boundary: 0.0001
- Iou Unlabelled: 0.4903
- Iou Sudoku-boundary: 0.0001

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:------------------------:|:--------------:|:-------------------:|
| 0.6034        | 3.33  | 20   | 0.6951          | 0.3427   | 0.5173        | 0.5149           | 0.6432              | 0.3914                   | 0.3940         | 0.2913              |
| 0.7796        | 6.67  | 40   | 0.7150          | 0.3049   | 0.5083        | 0.5021           | 0.8309              | 0.1857                   | 0.4501         | 0.1597              |
| 0.4378        | 10.0  | 60   | 0.9772          | 0.2452   | 0.5           | 0.4904           | 1.0                 | 0.0                      | 0.4904         | 0.0                 |
| 0.6804        | 13.33 | 80   | 1.1605          | 0.2452   | 0.5           | 0.4904           | 1.0                 | 0.0                      | 0.4904         | 0.0                 |
| 0.58          | 16.67 | 100  | 0.9787          | 0.2452   | 0.5           | 0.4904           | 1.0                 | 0.0                      | 0.4904         | 0.0                 |
| 0.6563        | 20.0  | 120  | 1.1860          | 0.2452   | 0.5           | 0.4904           | 1.0                 | 0.0                      | 0.4904         | 0.0                 |
| 0.5128        | 23.33 | 140  | 0.8884          | 0.2457   | 0.5002        | 0.4907           | 0.9996              | 0.0009                   | 0.4905         | 0.0009              |
| 0.5054        | 26.67 | 160  | 0.8746          | 0.2455   | 0.5002        | 0.4907           | 0.9998              | 0.0006                   | 0.4905         | 0.0006              |
| 0.5532        | 30.0  | 180  | 0.9540          | 0.2452   | 0.5000        | 0.4905           | 1.0                 | 0.0000                   | 0.4905         | 0.0000              |
| 0.3238        | 33.33 | 200  | 0.8916          | 0.2470   | 0.5009        | 0.4914           | 0.9984              | 0.0035                   | 0.4905         | 0.0035              |
| 0.2964        | 36.67 | 220  | 1.0162          | 0.2453   | 0.5000        | 0.4905           | 1.0000              | 0.0000                   | 0.4905         | 0.0000              |
| 0.2102        | 40.0  | 240  | 0.9650          | 0.2452   | 0.4998        | 0.4903           | 0.9996              | 0.0001                   | 0.4903         | 0.0001              |
| 0.623         | 43.33 | 260  | 0.9071          | 0.2461   | 0.5004        | 0.4909           | 0.9991              | 0.0017                   | 0.4904         | 0.0017              |
| 0.3741        | 46.67 | 280  | 0.9245          | 0.2454   | 0.5000        | 0.4904           | 0.9994              | 0.0006                   | 0.4903         | 0.0006              |
| 0.5765        | 50.0  | 300  | 0.9826          | 0.2452   | 0.4999        | 0.4903           | 0.9996              | 0.0001                   | 0.4903         | 0.0001              |


### Framework versions

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