README.md

metadata

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

defect-classifier-vit-base

This model is a fine-tuned version of 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