---
license: other
tags:
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-segments-toolwear
  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-segments-toolwear

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co./nvidia/mit-b0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0737
- Mean Iou: 0.3080
- Mean Accuracy: 0.6160
- Overall Accuracy: 0.6160
- Accuracy Unlabeled: nan
- Accuracy Wear: 0.6160
- Iou Unlabeled: 0.0
- Iou Wear: 0.6160

## 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 Unlabeled | Accuracy Wear | Iou Unlabeled | Iou Wear |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:--------:|
| 0.4694        | 3.33  | 20   | 0.4857          | 0.3178   | 0.6356        | 0.6356           | nan                | 0.6356        | 0.0           | 0.6356   |
| 0.3038        | 6.67  | 40   | 0.2805          | 0.3408   | 0.6816        | 0.6816           | nan                | 0.6816        | 0.0           | 0.6816   |
| 0.2303        | 10.0  | 60   | 0.2080          | 0.3408   | 0.6816        | 0.6816           | nan                | 0.6816        | 0.0           | 0.6816   |
| 0.1935        | 13.33 | 80   | 0.1870          | 0.3420   | 0.6841        | 0.6841           | nan                | 0.6841        | 0.0           | 0.6841   |
| 0.1697        | 16.67 | 100  | 0.1507          | 0.3405   | 0.6810        | 0.6810           | nan                | 0.6810        | 0.0           | 0.6810   |
| 0.1406        | 20.0  | 120  | 0.1377          | 0.3437   | 0.6874        | 0.6874           | nan                | 0.6874        | 0.0           | 0.6874   |
| 0.1363        | 23.33 | 140  | 0.1156          | 0.3301   | 0.6601        | 0.6601           | nan                | 0.6601        | 0.0           | 0.6601   |
| 0.117         | 26.67 | 160  | 0.1019          | 0.3376   | 0.6753        | 0.6753           | nan                | 0.6753        | 0.0           | 0.6753   |
| 0.0972        | 30.0  | 180  | 0.0935          | 0.3264   | 0.6529        | 0.6529           | nan                | 0.6529        | 0.0           | 0.6529   |
| 0.1076        | 33.33 | 200  | 0.0901          | 0.3292   | 0.6584        | 0.6584           | nan                | 0.6584        | 0.0           | 0.6584   |
| 0.0868        | 36.67 | 220  | 0.0806          | 0.3218   | 0.6436        | 0.6436           | nan                | 0.6436        | 0.0           | 0.6436   |
| 0.0866        | 40.0  | 240  | 0.0766          | 0.3183   | 0.6367        | 0.6367           | nan                | 0.6367        | 0.0           | 0.6367   |
| 0.0757        | 43.33 | 260  | 0.0750          | 0.3082   | 0.6165        | 0.6165           | nan                | 0.6165        | 0.0           | 0.6165   |
| 0.077         | 46.67 | 280  | 0.0750          | 0.3104   | 0.6207        | 0.6207           | nan                | 0.6207        | 0.0           | 0.6207   |
| 0.0765        | 50.0  | 300  | 0.0737          | 0.3080   | 0.6160        | 0.6160           | nan                | 0.6160        | 0.0           | 0.6160   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.13.3