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mit-b0-building-damage-lora

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Fine-Tuned from `ibrahim-601/mit-b0-building-damage-lora` on full data.

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  1. README.md +24 -24
  2. adapter_model.bin +1 -1
README.md CHANGED
@@ -15,14 +15,12 @@ should probably proofread and complete it, then remove this comment. -->
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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: 0.0719
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- - Mean Iou: 0.3422
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- - Mean Accuracy: 0.6845
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- - Overall Accuracy: 0.6845
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- - Accuracy Background: nan
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- - Accuracy Building: 0.6845
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- - Iou Background: 0.0
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- - Iou Building: 0.6845
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  ## Model description
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@@ -47,25 +45,27 @@ The following hyperparameters were used during training:
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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: 50
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Building | Iou Background | Iou Building |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-----------------:|:--------------:|:------------:|
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- | 0.0816 | 1.0 | 700 | 0.0954 | 0.3942 | 0.7884 | 0.7884 | nan | 0.7884 | 0.0 | 0.7884 |
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- | 0.0969 | 2.0 | 1400 | 0.0771 | 0.3662 | 0.7323 | 0.7323 | nan | 0.7323 | 0.0 | 0.7323 |
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- | 0.0813 | 3.0 | 2100 | 0.0735 | 0.3608 | 0.7216 | 0.7216 | nan | 0.7216 | 0.0 | 0.7216 |
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- | 0.0847 | 4.0 | 2800 | 0.0732 | 0.3557 | 0.7114 | 0.7114 | nan | 0.7114 | 0.0 | 0.7114 |
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- | 0.0657 | 5.0 | 3500 | 0.0705 | 0.3352 | 0.6703 | 0.6703 | nan | 0.6703 | 0.0 | 0.6703 |
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- | 0.0739 | 6.0 | 4200 | 0.0744 | 0.3606 | 0.7211 | 0.7211 | nan | 0.7211 | 0.0 | 0.7211 |
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- | 0.0642 | 7.0 | 4900 | 0.0737 | 0.3754 | 0.7508 | 0.7508 | nan | 0.7508 | 0.0 | 0.7508 |
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- | 0.0594 | 8.0 | 5600 | 0.0710 | 0.3128 | 0.6256 | 0.6256 | nan | 0.6256 | 0.0 | 0.6256 |
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- | 0.0694 | 9.0 | 6300 | 0.0702 | 0.3431 | 0.6863 | 0.6863 | nan | 0.6863 | 0.0 | 0.6863 |
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- | 0.0658 | 10.0 | 7000 | 0.0730 | 0.3666 | 0.7332 | 0.7332 | nan | 0.7332 | 0.0 | 0.7332 |
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- | 0.0849 | 11.0 | 7700 | 0.0827 | 0.4007 | 0.8013 | 0.8013 | nan | 0.8013 | 0.0 | 0.8013 |
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- | 0.0607 | 12.0 | 8400 | 0.0891 | 0.3844 | 0.7689 | 0.7689 | nan | 0.7689 | 0.0 | 0.7689 |
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- | 0.073 | 13.0 | 9100 | 0.1030 | 0.4268 | 0.8536 | 0.8536 | nan | 0.8536 | 0.0 | 0.8536 |
 
 
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  ### Framework versions
 
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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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  - 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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