---
license: other
base_model: nvidia/mit-b5
tags:
- generated_from_trainer
model-index:
- name: segcrack9k_conglomerate_segformer
  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. -->

# segcrack9k_conglomerate_segformer

This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0333
- Mean Iou: 0.3608
- Mean Accuracy: 0.7217
- Overall Accuracy: 0.7217
- Accuracy Background: nan
- Accuracy Crack: 0.7217
- Iou Background: 0.0
- Iou Crack: 0.7217

## 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: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Crack | Iou Background | Iou Crack |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:--------------:|:--------------:|:---------:|
| 0.0259        | 0.14  | 1000 | 0.0404          | 0.3267   | 0.6534        | 0.6534           | nan                 | 0.6534         | 0.0            | 0.6534    |
| 0.0186        | 0.27  | 2000 | 0.0378          | 0.3586   | 0.7172        | 0.7172           | nan                 | 0.7172         | 0.0            | 0.7172    |
| 0.0348        | 0.41  | 3000 | 0.0375          | 0.3209   | 0.6418        | 0.6418           | nan                 | 0.6418         | 0.0            | 0.6418    |
| 0.011         | 0.54  | 4000 | 0.0356          | 0.3496   | 0.6991        | 0.6991           | nan                 | 0.6991         | 0.0            | 0.6991    |
| 0.0132        | 0.68  | 5000 | 0.0350          | 0.3459   | 0.6918        | 0.6918           | nan                 | 0.6918         | 0.0            | 0.6918    |
| 0.0573        | 0.81  | 6000 | 0.0339          | 0.3575   | 0.7149        | 0.7149           | nan                 | 0.7149         | 0.0            | 0.7149    |
| 0.1466        | 0.95  | 7000 | 0.0333          | 0.3608   | 0.7217        | 0.7217           | nan                 | 0.7217         | 0.0            | 0.7217    |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3