| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: google/vit-base-patch16-224 |
| tags: |
| - image-classification |
| - vision |
| - defect-detection |
| - manufacturing-quality-control |
| - generated_from_trainer |
| datasets: |
| - imagefolder |
| metrics: |
| - accuracy |
| model-index: |
| - name: defect-classifier-vit-base |
| results: |
| - task: |
| name: Image Classification |
| type: image-classification |
| dataset: |
| name: MSherbinii/mvtec-ad-cable |
| type: imagefolder |
| config: default |
| split: None |
| args: default |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.9285714285714286 |
| --- |
| |
| <!-- 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. --> |
|
|
| # defect-classifier-vit-base |
|
|
| This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the MSherbinii/mvtec-ad-cable dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3193 |
| - Accuracy: 0.9286 |
| - Defect Precision: 0.9216 |
| - Defect Recall: 1.0 |
| - Defect F1: 0.9592 |
|
|
| ## 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: 5e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - num_epochs: 10 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Defect Precision | Defect Recall | Defect F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------------:|:-------------:|:---------:| |
| | 0.4932 | 1.0 | 17 | 0.4328 | 0.8393 | 0.8393 | 1.0 | 0.9126 | |
| | 0.4970 | 2.0 | 34 | 0.4307 | 0.8393 | 0.8393 | 1.0 | 0.9126 | |
| | 0.3777 | 3.0 | 51 | 0.3795 | 0.8393 | 0.8393 | 1.0 | 0.9126 | |
| | 0.3863 | 4.0 | 68 | 0.3475 | 0.8393 | 0.8393 | 1.0 | 0.9126 | |
| | 0.3000 | 5.0 | 85 | 0.3285 | 0.8393 | 0.9318 | 0.8723 | 0.9011 | |
| | 0.3114 | 6.0 | 102 | 0.4423 | 0.8393 | 0.8393 | 1.0 | 0.9126 | |
| | 0.2635 | 7.0 | 119 | 0.3369 | 0.8571 | 0.9333 | 0.8936 | 0.9130 | |
| | 0.2046 | 8.0 | 136 | 0.4730 | 0.7679 | 0.925 | 0.7872 | 0.8506 | |
| | 0.2625 | 9.0 | 153 | 0.3185 | 0.8929 | 0.9362 | 0.9362 | 0.9362 | |
| | 0.2306 | 10.0 | 170 | 0.3193 | 0.9286 | 0.9216 | 1.0 | 0.9592 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 5.12.1 |
| - Pytorch 2.12.1+cu130 |
| - Datasets 5.0.0 |
| - Tokenizers 0.22.2 |
| |
