---
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