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--- |
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license: other |
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base_model: nvidia/mit-b0 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: mit-b0-building-damage-lora |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# mit-b0-building-damage-lora |
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This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0661 |
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- Mean Iou: 0.3623 |
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- Mean Accuracy: 0.7245 |
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- Overall Accuracy: 0.7245 |
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- Accuracy Building: 0.7245 |
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- Iou Building: 0.7245 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0005 |
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- train_batch_size: 8 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Building | Iou Building | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-----------------:|:------------:| |
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| 0.0618 | 1.0 | 700 | 0.1463 | 0.4063 | 0.8125 | 0.8125 | 0.8125 | 0.8125 | |
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| 0.0813 | 2.0 | 1400 | 0.0861 | 0.3950 | 0.7900 | 0.7900 | 0.7900 | 0.7900 | |
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| 0.0715 | 3.0 | 2100 | 0.0856 | 0.3844 | 0.7689 | 0.7689 | 0.7689 | 0.7689 | |
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| 0.076 | 4.0 | 2800 | 0.1296 | 0.4161 | 0.8322 | 0.8322 | 0.8322 | 0.8322 | |
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| 0.0587 | 5.0 | 3500 | 0.0702 | 0.3078 | 0.6156 | 0.6156 | 0.6156 | 0.6156 | |
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| 0.0662 | 6.0 | 4200 | 0.0708 | 0.3613 | 0.7226 | 0.7226 | 0.7226 | 0.7226 | |
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| 0.059 | 7.0 | 4900 | 0.1063 | 0.4125 | 0.8249 | 0.8249 | 0.8249 | 0.8249 | |
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| 0.0532 | 8.0 | 5600 | 0.0693 | 0.3547 | 0.7094 | 0.7094 | 0.7094 | 0.7094 | |
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| 0.066 | 9.0 | 6300 | 0.0754 | 0.3932 | 0.7863 | 0.7863 | 0.7863 | 0.7863 | |
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| 0.0628 | 10.0 | 7000 | 0.0692 | 0.3874 | 0.7747 | 0.7747 | 0.7747 | 0.7747 | |
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| 0.0805 | 11.0 | 7700 | 0.0701 | 0.3896 | 0.7793 | 0.7793 | 0.7793 | 0.7793 | |
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| 0.0595 | 12.0 | 8400 | 0.0663 | 0.3774 | 0.7549 | 0.7549 | 0.7549 | 0.7549 | |
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| 0.0705 | 13.0 | 9100 | 0.0653 | 0.3717 | 0.7433 | 0.7433 | 0.7433 | 0.7433 | |
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| 0.071 | 14.0 | 9800 | 0.0651 | 0.3731 | 0.7461 | 0.7461 | 0.7461 | 0.7461 | |
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| 0.0656 | 15.0 | 10500 | 0.0648 | 0.3613 | 0.7227 | 0.7227 | 0.7227 | 0.7227 | |
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### Framework versions |
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- Transformers 4.33.0 |
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- Pytorch 2.0.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.13.3 |
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