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---
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
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