| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: google/vit-large-patch16-224 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - imagefolder |
| metrics: |
| - accuracy |
| - f1 |
| - precision |
| - recall |
| model-index: |
| - name: wool-classifier-finetuned |
| results: |
| - task: |
| name: Image Classification |
| type: image-classification |
| dataset: |
| name: imagefolder |
| type: imagefolder |
| config: default |
| split: validation |
| args: default |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.7777777777777778 |
| - name: F1 |
| type: f1 |
| value: 0.7681561135293505 |
| - name: Precision |
| type: precision |
| value: 0.7982514741774002 |
| - name: Recall |
| type: recall |
| value: 0.7777777777777778 |
| --- |
| |
| <!-- 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. --> |
|
|
| # wool-classifier-finetuned |
|
|
| This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the imagefolder dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.6767 |
| - Accuracy: 0.7778 |
| - F1: 0.7682 |
| - Precision: 0.7983 |
| - Recall: 0.7778 |
|
|
| ## 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: 3e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 8 |
| - 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: 15 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | 0.7426 | 1.0 | 45 | 0.7618 | 0.7284 | 0.7285 | 0.7981 | 0.7284 | |
| | 0.6744 | 2.0 | 90 | 0.8640 | 0.7284 | 0.7064 | 0.7190 | 0.7284 | |
| | 0.4237 | 3.0 | 135 | 0.6118 | 0.8148 | 0.8115 | 0.8309 | 0.8148 | |
| | 0.473 | 4.0 | 180 | 0.6418 | 0.8025 | 0.7843 | 0.8481 | 0.8025 | |
| | 0.3436 | 5.0 | 225 | 0.4420 | 0.8765 | 0.8606 | 0.8928 | 0.8765 | |
| | 0.2142 | 6.0 | 270 | 0.7575 | 0.7654 | 0.7508 | 0.8080 | 0.7654 | |
| | 0.2729 | 7.0 | 315 | 0.6660 | 0.7901 | 0.7768 | 0.8183 | 0.7901 | |
| | 0.3112 | 8.0 | 360 | 0.6767 | 0.7778 | 0.7682 | 0.7983 | 0.7778 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.55.4 |
| - Pytorch 2.8.0+cu126 |
| - Datasets 4.0.0 |
| - Tokenizers 0.21.4 |
| |
