---
license: other
base_model: nvidia/mit-b5
tags:
- generated_from_trainer
model-index:
- name: segcrack9k_conglomerate_train_test
  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_train_test

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: 0.0298
- Mean Iou: 0.3639
- Mean Accuracy: 0.7278
- Overall Accuracy: 0.7278
- Accuracy Background: nan
- Accuracy Crack: 0.7278
- Iou Background: 0.0
- Iou Crack: 0.7278

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:--------------:|:--------------:|:---------:|
| 0.0374        | 0.18  | 1000 | 0.0410          | 0.2472   | 0.4944        | 0.4944           | nan                 | 0.4944         | 0.0            | 0.4944    |
| 0.0337        | 0.36  | 2000 | 0.0341          | 0.3749   | 0.7497        | 0.7497           | nan                 | 0.7497         | 0.0            | 0.7497    |
| 0.0209        | 0.55  | 3000 | 0.0318          | 0.3335   | 0.6670        | 0.6670           | nan                 | 0.6670         | 0.0            | 0.6670    |
| 0.0099        | 0.73  | 4000 | 0.0315          | 0.3371   | 0.6743        | 0.6743           | nan                 | 0.6743         | 0.0            | 0.6743    |
| 0.026         | 0.91  | 5000 | 0.0298          | 0.3639   | 0.7278        | 0.7278           | nan                 | 0.7278         | 0.0            | 0.7278    |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3