---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: model
  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: 0.9867232343399052
---

<!-- 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. -->

# model

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.
It achieves the following results on the evaluation set:
- Loss: 0.0560
- Accuracy: 0.9867

## 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: 0.0002
- train_batch_size: 3
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|
| 0.1455        | 0.4279 | 10000 | 0.0871          | 0.9822   |
| 0.2997        | 0.8557 | 20000 | 0.0938          | 0.9748   |
| 0.0095        | 1.2836 | 30000 | 0.0787          | 0.9822   |
| 0.0047        | 1.7114 | 40000 | 0.0804          | 0.9799   |
| 0.0025        | 2.1393 | 50000 | 0.0647          | 0.9848   |
| 0.0085        | 2.5672 | 60000 | 0.0577          | 0.9864   |
| 0.0015        | 2.9950 | 70000 | 0.0618          | 0.9867   |
| 0.0996        | 3.4229 | 80000 | 0.0596          | 0.9869   |
| 0.0755        | 3.8508 | 90000 | 0.0560          | 0.9867   |


### Framework versions

- Transformers 4.48.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0