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segformer-b0-DeepCrack

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metadata
license: other
tags:
  - generated_from_trainer
model-index:
  - name: segformer-b0-DeepCrack
    results: []

segformer-b0-DeepCrack

This model is a fine-tuned version of nvidia/mit-b0 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3347
  • Mean Iou: 0.6839
  • Mean Accuracy: 0.7408
  • Overall Accuracy: 0.9681
  • Accuracy Background: 0.9897
  • Accuracy Crack: 0.4918
  • Iou Background: 0.9674
  • Iou Crack: 0.4003

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: 1

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Background Accuracy Crack Iou Background Iou Crack
0.8203 0.03 5 0.6973 0.3317 0.7410 0.5924 0.5783 0.9037 0.5758 0.0876
0.7469 0.07 10 0.6930 0.3533 0.7185 0.6325 0.6244 0.8125 0.6192 0.0873
0.7324 0.1 15 0.6884 0.3545 0.6605 0.6436 0.6421 0.6788 0.6329 0.0762
0.7079 0.13 20 0.6910 0.2537 0.5518 0.4726 0.4650 0.6386 0.4576 0.0498
0.6472 0.17 25 0.6831 0.2972 0.5734 0.5519 0.5498 0.5969 0.5400 0.0545
0.6344 0.2 30 0.6630 0.4652 0.7477 0.8045 0.8099 0.6854 0.7985 0.1318
0.6264 0.23 35 0.6389 0.5567 0.7850 0.8977 0.9084 0.6617 0.8947 0.2187
0.5811 0.27 40 0.6087 0.6070 0.8069 0.9279 0.9394 0.6745 0.9257 0.2882
0.5928 0.3 45 0.5584 0.6469 0.7851 0.9503 0.9660 0.6042 0.9490 0.3448
0.5312 0.33 50 0.5476 0.6508 0.7789 0.9527 0.9692 0.5886 0.9515 0.3502
0.5209 0.37 55 0.5423 0.6561 0.7665 0.9564 0.9744 0.5586 0.9553 0.3568
0.4675 0.4 60 0.5332 0.6470 0.7529 0.9553 0.9745 0.5313 0.9543 0.3397
0.4831 0.43 65 0.4772 0.6746 0.7502 0.9644 0.9847 0.5157 0.9636 0.3855
0.4512 0.47 70 0.4624 0.6734 0.7830 0.9598 0.9765 0.5895 0.9587 0.3881
0.426 0.5 75 0.4589 0.6688 0.7912 0.9572 0.9730 0.6094 0.9561 0.3815
0.4147 0.53 80 0.4529 0.6769 0.7846 0.9606 0.9773 0.5918 0.9596 0.3942
0.4144 0.57 85 0.4160 0.6767 0.7616 0.9635 0.9827 0.5405 0.9627 0.3908
0.4192 0.6 90 0.3747 0.6612 0.7271 0.9639 0.9863 0.4680 0.9631 0.3593
0.4294 0.63 95 0.3649 0.6495 0.7064 0.9637 0.9880 0.4247 0.9630 0.3359
0.3609 0.67 100 0.3730 0.6480 0.7003 0.9642 0.9893 0.4113 0.9636 0.3324
0.3782 0.7 105 0.3699 0.6584 0.7229 0.9637 0.9865 0.4592 0.9630 0.3538
0.3594 0.73 110 0.3505 0.6638 0.7161 0.9662 0.9899 0.4423 0.9656 0.3619
0.3966 0.77 115 0.3474 0.6720 0.7263 0.9670 0.9898 0.4627 0.9663 0.3776
0.3365 0.8 120 0.3598 0.6710 0.7185 0.9678 0.9915 0.4456 0.9672 0.3748
0.3497 0.83 125 0.3530 0.6752 0.7161 0.9692 0.9932 0.4389 0.9686 0.3817
0.3303 0.87 130 0.3424 0.6792 0.7247 0.9690 0.9922 0.4572 0.9684 0.3899
0.3702 0.9 135 0.3379 0.6823 0.7341 0.9686 0.9908 0.4774 0.9679 0.3967
0.3199 0.93 140 0.3317 0.6858 0.7468 0.9678 0.9888 0.5048 0.9671 0.4044
0.304 0.97 145 0.3189 0.6854 0.7408 0.9685 0.9900 0.4916 0.9678 0.4030
0.3392 1.0 150 0.3347 0.6839 0.7408 0.9681 0.9897 0.4918 0.9674 0.4003

Framework versions

  • Transformers 4.30.2
  • Pytorch 2.0.1+cu118
  • Datasets 2.13.1
  • Tokenizers 0.13.3