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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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- f1
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- precision
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- recall
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model-index:
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- name: vit-base-patch16-224-in21k-bridgedefectVIT15
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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:
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accuracy: 0.9573153608536927
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- name: F1
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type: f1
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value:
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f1: 0.9566147291413047
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- name: Precision
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type: precision
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value:
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precision: 0.9591127716274309
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- name: Recall
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type: recall
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value:
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recall: 0.9565472623176632
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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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# vit-base-patch16-224-in21k-bridgedefectVIT15
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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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- Loss: 0.2402
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- Accuracy: {'accuracy': 0.9573153608536927}
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- F1: {'f1': 0.9566147291413047}
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- Precision: {'precision': 0.9591127716274309}
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- Recall: {'recall': 0.9565472623176632}
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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: 5e-05
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- train_batch_size: 2
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- eval_batch_size: 2
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 8
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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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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 15
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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|:-------------:|:-----:|:-----:|:---------------:|:--------------------------------:|:--------------------------:|:---------------------------------:|:------------------------------:|
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| 0.3548 | 1.0 | 1780 | 0.2848 | {'accuracy': 0.9118225217635496} | {'f1': 0.912598515170384} | {'precision': 0.913326374297146} | {'recall': 0.9157022464716918} |
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| 0.1718 | 2.0 | 3560 | 0.3435 | {'accuracy': 0.9005897219882055} | {'f1': 0.9021520907258462} | {'precision': 0.9071588887385811} | {'recall': 0.9088734326741875} |
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| 0.1956 | 3.0 | 5340 | 0.2290 | {'accuracy': 0.9337264813254704} | {'f1': 0.9345043308561282} | {'precision': 0.9371641968965463} | {'recall': 0.9353444695340449} |
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| 0.1589 | 4.0 | 7120 | 0.3518 | {'accuracy': 0.925582701488346} | {'f1': 0.9240312800580016} | {'precision': 0.9310407182465765} | {'recall': 0.9241275251443595} |
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| 0.1076 | 5.0 | 8900 | 0.4017 | {'accuracy': 0.9188430216231396} | {'f1': 0.9170326424426785} | {'precision': 0.923800610078333} | {'recall': 0.9181896594596475} |
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| 0.0895 | 6.0 | 10680 | 0.2950 | {'accuracy': 0.938219601235608} | {'f1': 0.9380460882172743} | {'precision': 0.9406510771971466} | {'recall': 0.9398150744796098} |
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| 0.0833 | 7.0 | 12460 | 0.1882 | {'accuracy': 0.9559112608817748} | {'f1': 0.9553785330080078} | {'precision': 0.957564211420095} | {'recall': 0.9550045684543612} |
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| 0.034 | 8.0 | 14240 | 0.3222 | {'accuracy': 0.9401853411962932} | {'f1': 0.9401162584753809} | {'precision': 0.944463542451817} | {'recall': 0.9410746120960137} |
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| 0.1117 | 9.0 | 16020 | 0.3084 | {'accuracy': 0.9401853411962932} | {'f1': 0.9389336455514373} | {'precision': 0.945493350000876} | {'recall': 0.9374486305327216} |
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| 0.2057 | 10.0 | 17800 | 0.3612 | {'accuracy': 0.9348497613030048} | {'f1': 0.9343390020827073} | {'precision': 0.939876035403298} | {'recall': 0.9348316142752356} |
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| 0.1 | 11.0 | 19580 | 0.2284 | {'accuracy': 0.9553496208930076} | {'f1': 0.9540937018628736} | {'precision': 0.9563364479044711} | {'recall': 0.9537814730817218} |
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| 0.0531 | 12.0 | 21360 | 0.2393 | {'accuracy': 0.9528222409435552} | {'f1': 0.9517895350619009} | {'precision': 0.955245168398952} | {'recall': 0.9514588091149371} |
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| 0.0597 | 13.0 | 23140 | 0.2695 | {'accuracy': 0.9519797809604044} | {'f1': 0.9513321647748849} | {'precision': 0.9541412213348108} | {'recall': 0.9515688542696423} |
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| 0.0482 | 14.0 | 24920 | 0.2403 | {'accuracy': 0.9567537208649256} | {'f1': 0.9560207781245073} | {'precision': 0.9590114685856663} | {'recall': 0.9557731012948057} |
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| 0.0019 | 15.0 | 26700 | 0.2402 | {'accuracy': 0.9573153608536927} | {'f1': 0.9566147291413047} | {'precision': 0.9591127716274309} | {'recall': 0.9565472623176632} |
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### Framework versions
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- Transformers 4.37.2
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- Pytorch 2.1.0
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- Datasets 2.17.1
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- Tokenizers 0.15.2
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