| --- |
| license: other |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: segformer-b0-DeepCrack |
| 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. --> |
|
|
| # segformer-b0-DeepCrack |
|
|
| This model is a fine-tuned version of [nvidia/mit-b4](https://huggingface.co/nvidia/mit-b4) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0017 |
| - Mean Iou: 0.0 |
| - Mean Accuracy: 0.0 |
| - Overall Accuracy: 0.0 |
| - Accuracy Background: nan |
| - Accuracy Cracked: 0.0 |
| - Iou Background: 0.0 |
| - Iou Cracked: 0.0 |
|
|
| ## 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: 4 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Cracked | Iou Background | Iou Cracked | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:----------------:|:--------------:|:-----------:| |
| | 0.2923 | 0.13 | 20 | 0.2120 | 0.0200 | 0.0399 | 0.0399 | nan | 0.0399 | 0.0 | 0.0399 | |
| | 0.0959 | 0.27 | 40 | 0.0702 | 0.0661 | 0.1321 | 0.1321 | nan | 0.1321 | 0.0 | 0.1321 | |
| | 0.0316 | 0.4 | 60 | 0.0378 | 0.0193 | 0.0387 | 0.0387 | nan | 0.0387 | 0.0 | 0.0387 | |
| | 0.0184 | 0.53 | 80 | 0.0165 | 0.0306 | 0.0612 | 0.0612 | nan | 0.0612 | 0.0 | 0.0612 | |
| | 0.0119 | 0.67 | 100 | 0.0108 | 0.0277 | 0.0554 | 0.0554 | nan | 0.0554 | 0.0 | 0.0554 | |
| | 0.0083 | 0.8 | 120 | 0.0085 | 0.0381 | 0.0761 | 0.0761 | nan | 0.0761 | 0.0 | 0.0761 | |
| | 0.0085 | 0.93 | 140 | 0.0118 | 0.0112 | 0.0223 | 0.0223 | nan | 0.0223 | 0.0 | 0.0223 | |
| | 0.0072 | 1.07 | 160 | 0.0063 | 0.0289 | 0.0578 | 0.0578 | nan | 0.0578 | 0.0 | 0.0578 | |
| | 0.0072 | 1.2 | 180 | 0.0057 | 0.0004 | 0.0009 | 0.0009 | nan | 0.0009 | 0.0 | 0.0009 | |
| | 0.0038 | 1.33 | 200 | 0.0037 | 0.0004 | 0.0009 | 0.0009 | nan | 0.0009 | 0.0 | 0.0009 | |
| | 0.0038 | 1.47 | 220 | 0.0035 | 0.0024 | 0.0048 | 0.0048 | nan | 0.0048 | 0.0 | 0.0048 | |
| | 0.0037 | 1.6 | 240 | 0.0033 | 0.0035 | 0.0071 | 0.0071 | nan | 0.0071 | 0.0 | 0.0071 | |
| | 0.004 | 1.73 | 260 | 0.0029 | 0.0000 | 0.0000 | 0.0000 | nan | 0.0000 | 0.0 | 0.0000 | |
| | 0.0027 | 1.87 | 280 | 0.0027 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | |
| | 0.0029 | 2.0 | 300 | 0.0025 | 0.0000 | 0.0000 | 0.0000 | nan | 0.0000 | 0.0 | 0.0000 | |
| | 0.0032 | 2.13 | 320 | 0.0026 | 0.0000 | 0.0000 | 0.0000 | nan | 0.0000 | 0.0 | 0.0000 | |
| | 0.0024 | 2.27 | 340 | 0.0023 | 0.0000 | 0.0000 | 0.0000 | nan | 0.0000 | 0.0 | 0.0000 | |
| | 0.0021 | 2.4 | 360 | 0.0024 | 0.0000 | 0.0000 | 0.0000 | nan | 0.0000 | 0.0 | 0.0000 | |
| | 0.0021 | 2.53 | 380 | 0.0021 | 0.0000 | 0.0000 | 0.0000 | nan | 0.0000 | 0.0 | 0.0000 | |
| | 0.0026 | 2.67 | 400 | 0.0020 | 0.0000 | 0.0001 | 0.0001 | nan | 0.0001 | 0.0 | 0.0001 | |
| | 0.002 | 2.8 | 420 | 0.0018 | 0.0000 | 0.0000 | 0.0000 | nan | 0.0000 | 0.0 | 0.0000 | |
| | 0.0019 | 2.93 | 440 | 0.0020 | 0.0000 | 0.0000 | 0.0000 | nan | 0.0000 | 0.0 | 0.0000 | |
| | 0.0023 | 3.07 | 460 | 0.0020 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | |
| | 0.002 | 3.2 | 480 | 0.0019 | 0.0000 | 0.0000 | 0.0000 | nan | 0.0000 | 0.0 | 0.0000 | |
| | 0.0018 | 3.33 | 500 | 0.0019 | 0.0000 | 0.0001 | 0.0001 | nan | 0.0001 | 0.0 | 0.0001 | |
| | 0.0018 | 3.47 | 520 | 0.0018 | 0.0000 | 0.0001 | 0.0001 | nan | 0.0001 | 0.0 | 0.0001 | |
| | 0.0021 | 3.6 | 540 | 0.0017 | 0.0000 | 0.0000 | 0.0000 | nan | 0.0000 | 0.0 | 0.0000 | |
| | 0.0018 | 3.73 | 560 | 0.0017 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | |
| | 0.0017 | 3.87 | 580 | 0.0016 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | |
| | 0.002 | 4.0 | 600 | 0.0017 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.30.2 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 2.13.1 |
| - Tokenizers 0.13.3 |
|
|
