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new_ecc_segformer

Image Segmentation
Transformers
PyTorch
segformer
vision
Generated from Trainer
Inference Endpoints
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new_ecc_segformer / README.md
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metadata
license: other
base_model: nvidia/mit-b5
tags:
  - image-segmentation
  - vision
  - generated_from_trainer
model-index:
  - name: new_ecc_segformer
    results: []

new_ecc_segformer

This model is a fine-tuned version of nvidia/mit-b5 on the rishitunu/ECC_crackdataset_withsplit dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0663
  • Mean Iou: 0.1943
  • Mean Accuracy: 0.3915
  • Overall Accuracy: 0.3915
  • Accuracy Background: nan
  • Accuracy Crack: 0.3915
  • Iou Background: 0.0
  • Iou Crack: 0.3887

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: 1337
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: polynomial
  • training_steps: 10000

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Background Accuracy Crack Iou Background Iou Crack
0.0489 1.0 438 0.0634 0.1464 0.2933 0.2933 nan 0.2933 0.0 0.2929
0.0542 2.0 876 0.0439 0.1956 0.3917 0.3917 nan 0.3917 0.0 0.3912
0.0484 3.0 1314 0.0434 0.1719 0.3551 0.3551 nan 0.3551 0.0 0.3439
0.0539 4.0 1752 0.0447 0.1871 0.3820 0.3820 nan 0.3820 0.0 0.3741
0.0565 5.0 2190 0.0435 0.1888 0.3937 0.3937 nan 0.3937 0.0 0.3777
0.0544 6.0 2628 0.0442 0.1904 0.3930 0.3930 nan 0.3930 0.0 0.3808
0.0421 7.0 3066 0.0449 0.2256 0.4651 0.4651 nan 0.4651 0.0 0.4513
0.0352 8.0 3504 0.0587 0.1569 0.3165 0.3165 nan 0.3165 0.0 0.3138
0.0394 9.0 3942 0.0442 0.1842 0.3710 0.3710 nan 0.3710 0.0 0.3684
0.0445 10.0 4380 0.0609 0.1167 0.4173 0.4173 nan 0.4173 0.0 0.2334
0.0503 11.0 4818 0.0504 0.1702 0.3714 0.3714 nan 0.3714 0.0 0.3403
0.0379 12.0 5256 0.0460 0.1903 0.3869 0.3869 nan 0.3869 0.0 0.3807
0.0405 13.0 5694 0.0452 0.2017 0.4084 0.4084 nan 0.4084 0.0 0.4034
0.0367 14.0 6132 0.0477 0.1995 0.4060 0.4060 nan 0.4060 0.0 0.3990
0.0315 15.0 6570 0.0498 0.2073 0.4208 0.4208 nan 0.4208 0.0 0.4147
0.0244 16.0 7008 0.0486 0.1963 0.4029 0.4029 nan 0.4029 0.0 0.3926
0.031 17.0 7446 0.0568 0.1927 0.3892 0.3892 nan 0.3892 0.0 0.3855
0.0288 18.0 7884 0.0560 0.2033 0.4092 0.4092 nan 0.4092 0.0 0.4067
0.0354 19.0 8322 0.0613 0.2007 0.4056 0.4056 nan 0.4056 0.0 0.4013
0.0315 20.0 8760 0.0605 0.1865 0.3752 0.3752 nan 0.3752 0.0 0.3731
0.0343 21.0 9198 0.0653 0.1991 0.4019 0.4019 nan 0.4019 0.0 0.3981
0.0327 22.0 9636 0.0660 0.1945 0.3924 0.3924 nan 0.3924 0.0 0.3891
0.0252 22.83 10000 0.0663 0.1943 0.3915 0.3915 nan 0.3915 0.0 0.3887

Framework versions

  • Transformers 4.34.1
  • Pytorch 2.1.0+cpu
  • Datasets 2.14.6
  • Tokenizers 0.14.1