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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: 0.0661
- 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
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