---
license: other
base_model: nvidia/mit-b0
tags:
- generated_from_trainer
model-index:
- name: mit-b0-building-damage-lora
  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. -->

# mit-b0-building-damage-lora

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: 0.0661
- Mean Iou: 0.3623
- Mean Accuracy: 0.7245
- Overall Accuracy: 0.7245
- Accuracy Building: 0.7245
- Iou Building: 0.7245

## 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: 0.0005
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Building | Iou Building |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-----------------:|:------------:|
| 0.0618        | 1.0   | 700   | 0.1463          | 0.4063   | 0.8125        | 0.8125           | 0.8125            | 0.8125       |
| 0.0813        | 2.0   | 1400  | 0.0861          | 0.3950   | 0.7900        | 0.7900           | 0.7900            | 0.7900       |
| 0.0715        | 3.0   | 2100  | 0.0856          | 0.3844   | 0.7689        | 0.7689           | 0.7689            | 0.7689       |
| 0.076         | 4.0   | 2800  | 0.1296          | 0.4161   | 0.8322        | 0.8322           | 0.8322            | 0.8322       |
| 0.0587        | 5.0   | 3500  | 0.0702          | 0.3078   | 0.6156        | 0.6156           | 0.6156            | 0.6156       |
| 0.0662        | 6.0   | 4200  | 0.0708          | 0.3613   | 0.7226        | 0.7226           | 0.7226            | 0.7226       |
| 0.059         | 7.0   | 4900  | 0.1063          | 0.4125   | 0.8249        | 0.8249           | 0.8249            | 0.8249       |
| 0.0532        | 8.0   | 5600  | 0.0693          | 0.3547   | 0.7094        | 0.7094           | 0.7094            | 0.7094       |
| 0.066         | 9.0   | 6300  | 0.0754          | 0.3932   | 0.7863        | 0.7863           | 0.7863            | 0.7863       |
| 0.0628        | 10.0  | 7000  | 0.0692          | 0.3874   | 0.7747        | 0.7747           | 0.7747            | 0.7747       |
| 0.0805        | 11.0  | 7700  | 0.0701          | 0.3896   | 0.7793        | 0.7793           | 0.7793            | 0.7793       |
| 0.0595        | 12.0  | 8400  | 0.0663          | 0.3774   | 0.7549        | 0.7549           | 0.7549            | 0.7549       |
| 0.0705        | 13.0  | 9100  | 0.0653          | 0.3717   | 0.7433        | 0.7433           | 0.7433            | 0.7433       |
| 0.071         | 14.0  | 9800  | 0.0651          | 0.3731   | 0.7461        | 0.7461           | 0.7461            | 0.7461       |
| 0.0656        | 15.0  | 10500 | 0.0648          | 0.3613   | 0.7227        | 0.7227           | 0.7227            | 0.7227       |


### Framework versions

- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3