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---
license: other
base_model: nvidia/mit-b0
tags:
- generated_from_trainer
model-index:
- name: CrackSeg-MIT-b0-aug
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. -->
# CrackSeg-MIT-b0-aug
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0578
- Mean Iou: 0.3169
- Mean Accuracy: 0.6337
- Overall Accuracy: 0.6337
- Accuracy Background: nan
- Accuracy Crack: 0.6337
- Iou Background: 0.0
- Iou Crack: 0.6337
## 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: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Crack | Iou Background | Iou Crack |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:--------------:|:--------------:|:---------:|
| 0.2102 | 0.04 | 100 | 0.1362 | 0.1116 | 0.2232 | 0.2232 | nan | 0.2232 | 0.0 | 0.2232 |
| 0.065 | 0.08 | 200 | 0.1125 | 0.0153 | 0.0305 | 0.0305 | nan | 0.0305 | 0.0 | 0.0305 |
| 0.1738 | 0.12 | 300 | 0.1165 | 0.1976 | 0.3953 | 0.3953 | nan | 0.3953 | 0.0 | 0.3953 |
| 0.0476 | 0.17 | 400 | 0.1979 | 0.0120 | 0.0241 | 0.0241 | nan | 0.0241 | 0.0 | 0.0241 |
| 0.0524 | 0.21 | 500 | 0.1063 | 0.0533 | 0.1066 | 0.1066 | nan | 0.1066 | 0.0 | 0.1066 |
| 0.0496 | 0.25 | 600 | 0.1154 | 0.1646 | 0.3292 | 0.3292 | nan | 0.3292 | 0.0 | 0.3292 |
| 0.0497 | 0.29 | 700 | 0.0795 | 0.3184 | 0.6368 | 0.6368 | nan | 0.6368 | 0.0 | 0.6368 |
| 0.032 | 0.33 | 800 | 0.0905 | 0.1792 | 0.3583 | 0.3583 | nan | 0.3583 | 0.0 | 0.3583 |
| 0.1207 | 0.37 | 900 | 0.0738 | 0.2401 | 0.4802 | 0.4802 | nan | 0.4802 | 0.0 | 0.4802 |
| 0.0511 | 0.41 | 1000 | 0.0883 | 0.2591 | 0.5182 | 0.5182 | nan | 0.5182 | 0.0 | 0.5182 |
| 0.0264 | 0.46 | 1100 | 0.0815 | 0.1655 | 0.3309 | 0.3309 | nan | 0.3309 | 0.0 | 0.3309 |
| 0.0719 | 0.5 | 1200 | 0.0772 | 0.3040 | 0.6080 | 0.6080 | nan | 0.6080 | 0.0 | 0.6080 |
| 0.042 | 0.54 | 1300 | 0.0707 | 0.2797 | 0.5593 | 0.5593 | nan | 0.5593 | 0.0 | 0.5593 |
| 0.167 | 0.58 | 1400 | 0.0685 | 0.3609 | 0.7218 | 0.7218 | nan | 0.7218 | 0.0 | 0.7218 |
| 0.0206 | 0.62 | 1500 | 0.0655 | 0.2469 | 0.4937 | 0.4937 | nan | 0.4937 | 0.0 | 0.4937 |
| 0.0211 | 0.66 | 1600 | 0.0937 | 0.3334 | 0.6668 | 0.6668 | nan | 0.6668 | 0.0 | 0.6668 |
| 0.0659 | 0.7 | 1700 | 0.0750 | 0.2382 | 0.4764 | 0.4764 | nan | 0.4764 | 0.0 | 0.4764 |
| 0.0478 | 0.75 | 1800 | 0.0693 | 0.2944 | 0.5888 | 0.5888 | nan | 0.5888 | 0.0 | 0.5888 |
| 0.0287 | 0.79 | 1900 | 0.0710 | 0.2395 | 0.4790 | 0.4790 | nan | 0.4790 | 0.0 | 0.4790 |
| 0.0359 | 0.83 | 2000 | 0.0580 | 0.3385 | 0.6771 | 0.6771 | nan | 0.6771 | 0.0 | 0.6771 |
| 0.0309 | 0.87 | 2100 | 0.0744 | 0.2153 | 0.4305 | 0.4305 | nan | 0.4305 | 0.0 | 0.4305 |
| 0.0039 | 0.91 | 2200 | 0.0636 | 0.2974 | 0.5947 | 0.5947 | nan | 0.5947 | 0.0 | 0.5947 |
| 0.0152 | 0.95 | 2300 | 0.0635 | 0.3215 | 0.6430 | 0.6430 | nan | 0.6430 | 0.0 | 0.6430 |
| 0.0233 | 0.99 | 2400 | 0.0668 | 0.3039 | 0.6077 | 0.6077 | nan | 0.6077 | 0.0 | 0.6077 |
| 0.0088 | 1.04 | 2500 | 0.0673 | 0.3352 | 0.6704 | 0.6704 | nan | 0.6704 | 0.0 | 0.6704 |
| 0.0756 | 1.08 | 2600 | 0.0599 | 0.3310 | 0.6621 | 0.6621 | nan | 0.6621 | 0.0 | 0.6621 |
| 0.0522 | 1.12 | 2700 | 0.0674 | 0.2943 | 0.5885 | 0.5885 | nan | 0.5885 | 0.0 | 0.5885 |
| 0.0595 | 1.16 | 2800 | 0.0828 | 0.2382 | 0.4763 | 0.4763 | nan | 0.4763 | 0.0 | 0.4763 |
| 0.0135 | 1.2 | 2900 | 0.0574 | 0.2901 | 0.5802 | 0.5802 | nan | 0.5802 | 0.0 | 0.5802 |
| 0.0289 | 1.24 | 3000 | 0.0700 | 0.3186 | 0.6372 | 0.6372 | nan | 0.6372 | 0.0 | 0.6372 |
| 0.0403 | 1.28 | 3100 | 0.0761 | 0.3741 | 0.7483 | 0.7483 | nan | 0.7483 | 0.0 | 0.7483 |
| 0.0131 | 1.33 | 3200 | 0.0600 | 0.3285 | 0.6570 | 0.6570 | nan | 0.6570 | 0.0 | 0.6570 |
| 0.0957 | 1.37 | 3300 | 0.0633 | 0.3400 | 0.6801 | 0.6801 | nan | 0.6801 | 0.0 | 0.6801 |
| 0.0152 | 1.41 | 3400 | 0.0678 | 0.3479 | 0.6958 | 0.6958 | nan | 0.6958 | 0.0 | 0.6958 |
| 0.0235 | 1.45 | 3500 | 0.0636 | 0.3416 | 0.6832 | 0.6832 | nan | 0.6832 | 0.0 | 0.6832 |
| 0.0304 | 1.49 | 3600 | 0.0596 | 0.3606 | 0.7211 | 0.7211 | nan | 0.7211 | 0.0 | 0.7211 |
| 0.0012 | 1.53 | 3700 | 0.0605 | 0.2992 | 0.5983 | 0.5983 | nan | 0.5983 | 0.0 | 0.5983 |
| 0.0435 | 1.57 | 3800 | 0.0563 | 0.3283 | 0.6566 | 0.6566 | nan | 0.6566 | 0.0 | 0.6566 |
| 0.05 | 1.61 | 3900 | 0.0601 | 0.3314 | 0.6628 | 0.6628 | nan | 0.6628 | 0.0 | 0.6628 |
| 0.063 | 1.66 | 4000 | 0.0617 | 0.3307 | 0.6614 | 0.6614 | nan | 0.6614 | 0.0 | 0.6614 |
| 0.0552 | 1.7 | 4100 | 0.0626 | 0.3580 | 0.7161 | 0.7161 | nan | 0.7161 | 0.0 | 0.7161 |
| 0.0153 | 1.74 | 4200 | 0.0622 | 0.2864 | 0.5728 | 0.5728 | nan | 0.5728 | 0.0 | 0.5728 |
| 0.0446 | 1.78 | 4300 | 0.0612 | 0.3224 | 0.6448 | 0.6448 | nan | 0.6448 | 0.0 | 0.6448 |
| 0.0203 | 1.82 | 4400 | 0.0589 | 0.3167 | 0.6334 | 0.6334 | nan | 0.6334 | 0.0 | 0.6334 |
| 0.0424 | 1.86 | 4500 | 0.0567 | 0.3443 | 0.6887 | 0.6887 | nan | 0.6887 | 0.0 | 0.6887 |
| 0.0103 | 1.9 | 4600 | 0.0591 | 0.3282 | 0.6563 | 0.6563 | nan | 0.6563 | 0.0 | 0.6563 |
| 0.0831 | 1.95 | 4700 | 0.0573 | 0.3224 | 0.6447 | 0.6447 | nan | 0.6447 | 0.0 | 0.6447 |
| 0.1301 | 1.99 | 4800 | 0.0578 | 0.3169 | 0.6337 | 0.6337 | nan | 0.6337 | 0.0 | 0.6337 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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