---
license: other
base_model: nvidia/mit-b5
tags:
- generated_from_trainer
model-index:
- name: segcrack9k_conglomerate_segformer_aug
  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_aug

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: 0.0362
- Mean Iou: 0.3412
- Mean Accuracy: 0.6823
- Overall Accuracy: 0.6823
- Accuracy Background: nan
- Accuracy Crack: 0.6823
- Iou Background: 0.0
- Iou Crack: 0.6823

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:--------------:|:--------------:|:---------:|
| 0.0323        | 0.14  | 1000 | 0.0445          | 0.3573   | 0.7146        | 0.7146           | nan                 | 0.7146         | 0.0            | 0.7146    |
| 0.0222        | 0.27  | 2000 | 0.0394          | 0.3591   | 0.7181        | 0.7181           | nan                 | 0.7181         | 0.0            | 0.7181    |
| 0.0335        | 0.41  | 3000 | 0.0404          | 0.2907   | 0.5813        | 0.5813           | nan                 | 0.5813         | 0.0            | 0.5813    |
| 0.013         | 0.54  | 4000 | 0.0384          | 0.3244   | 0.6489        | 0.6489           | nan                 | 0.6489         | 0.0            | 0.6489    |
| 0.0159        | 0.68  | 5000 | 0.0382          | 0.3088   | 0.6176        | 0.6176           | nan                 | 0.6176         | 0.0            | 0.6176    |
| 0.0608        | 0.81  | 6000 | 0.0366          | 0.3251   | 0.6502        | 0.6502           | nan                 | 0.6502         | 0.0            | 0.6502    |
| 0.1738        | 0.95  | 7000 | 0.0362          | 0.3412   | 0.6823        | 0.6823           | nan                 | 0.6823         | 0.0            | 0.6823    |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3