---
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: 0.0719
- Mean Iou: 0.3422
- Mean Accuracy: 0.6845
- Overall Accuracy: 0.6845
- Accuracy Background: nan
- Accuracy Building: 0.6845
- Iou Background: 0.0
- Iou Building: 0.6845

## 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Building | Iou Background | Iou Building |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-----------------:|:--------------:|:------------:|
| 0.0816        | 1.0   | 700  | 0.0954          | 0.3942   | 0.7884        | 0.7884           | nan                 | 0.7884            | 0.0            | 0.7884       |
| 0.0969        | 2.0   | 1400 | 0.0771          | 0.3662   | 0.7323        | 0.7323           | nan                 | 0.7323            | 0.0            | 0.7323       |
| 0.0813        | 3.0   | 2100 | 0.0735          | 0.3608   | 0.7216        | 0.7216           | nan                 | 0.7216            | 0.0            | 0.7216       |
| 0.0847        | 4.0   | 2800 | 0.0732          | 0.3557   | 0.7114        | 0.7114           | nan                 | 0.7114            | 0.0            | 0.7114       |
| 0.0657        | 5.0   | 3500 | 0.0705          | 0.3352   | 0.6703        | 0.6703           | nan                 | 0.6703            | 0.0            | 0.6703       |
| 0.0739        | 6.0   | 4200 | 0.0744          | 0.3606   | 0.7211        | 0.7211           | nan                 | 0.7211            | 0.0            | 0.7211       |
| 0.0642        | 7.0   | 4900 | 0.0737          | 0.3754   | 0.7508        | 0.7508           | nan                 | 0.7508            | 0.0            | 0.7508       |
| 0.0594        | 8.0   | 5600 | 0.0710          | 0.3128   | 0.6256        | 0.6256           | nan                 | 0.6256            | 0.0            | 0.6256       |
| 0.0694        | 9.0   | 6300 | 0.0702          | 0.3431   | 0.6863        | 0.6863           | nan                 | 0.6863            | 0.0            | 0.6863       |
| 0.0658        | 10.0  | 7000 | 0.0730          | 0.3666   | 0.7332        | 0.7332           | nan                 | 0.7332            | 0.0            | 0.7332       |
| 0.0849        | 11.0  | 7700 | 0.0827          | 0.4007   | 0.8013        | 0.8013           | nan                 | 0.8013            | 0.0            | 0.8013       |
| 0.0607        | 12.0  | 8400 | 0.0891          | 0.3844   | 0.7689        | 0.7689           | nan                 | 0.7689            | 0.0            | 0.7689       |
| 0.073         | 13.0  | 9100 | 0.1030          | 0.4268   | 0.8536        | 0.8536           | nan                 | 0.8536            | 0.0            | 0.8536       |


### Framework versions

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