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Fine-Tuned from `ibrahim-601/mit-b0-building-damage-lora` on full data.
dd19712
metadata
license: other
base_model: nvidia/mit-b0
tags:
  - generated_from_trainer
model-index:
  - name: mit-b0-building-damage-lora
    results: []

mit-b0-building-damage-lora

This model is a fine-tuned version of 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