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README.md
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---
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license: apache-2.0
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base_model: google/vit-base-patch16-224-in21k
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tags:
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- generated_from_trainer
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datasets:
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- imagefolder
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metrics:
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- accuracy
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model-index:
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- name: CrackDetectionLowRes
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results:
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- task:
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name: Image Classification
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type: image-classification
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dataset:
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name: imagefolder
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type: imagefolder
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config: default
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split: train
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args: default
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.9940476190476191
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# CrackDetectionLowRes
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Accuracy: 0.9940
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- Loss: 0.0183
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 1337
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 5.0
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### Training results
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| Training Loss | Epoch | Step | Accuracy | Validation Loss |
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|:-------------:|:-----:|:----:|:--------:|:---------------:|
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| 0.0126 | 1.0 | 992 | 0.9879 | 0.0344 |
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| 0.0788 | 2.0 | 1904 | 0.9933 | 0.0220 |
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| 0.1336 | 3.0 | 2856 | 0.9933 | 0.0222 |
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| 0.0066 | 4.0 | 3808 | 0.9933 | 0.0190 |
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| 0.0528 | 5.0 | 4760 | 0.9940 | 0.0183 |
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### Framework versions
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- Transformers 4.31.0.dev0
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- Pytorch 2.0.1+cpu
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- Datasets 2.13.1
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- Tokenizers 0.13.3
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