---
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: vit-finetuned-chessman
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9818181818181818
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# vit-finetuned-chessman

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: 0.0779
- Accuracy: 0.9818

## 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: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0097        | 1.0   | 28   | 0.0592          | 0.9818   |
| 0.0073        | 2.0   | 56   | 0.0949          | 0.9636   |
| 0.0058        | 3.0   | 84   | 0.0886          | 0.9818   |
| 0.0047        | 4.0   | 112  | 0.0779          | 0.9818   |


### Framework versions

- Transformers 4.57.1
- Pytorch 2.8.0
- Datasets 4.4.1
- Tokenizers 0.22.1