|
--- |
|
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 |
|
|
