---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Chess
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train[:258]
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6538461538461539
---

<!-- 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. -->

# Chess

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: 0.7292
- Accuracy: 0.6538

## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.92  | 3    | 1.0620          | 0.5385   |
| No log        | 1.85  | 6    | 0.9886          | 0.5962   |
| No log        | 2.77  | 9    | 0.9286          | 0.7115   |
| 0.9947        | 4.0   | 13   | 0.8659          | 0.6731   |
| 0.9947        | 4.92  | 16   | 0.8310          | 0.6731   |
| 0.9947        | 5.85  | 19   | 0.7778          | 0.6731   |
| 0.7638        | 6.77  | 22   | 0.7388          | 0.7115   |
| 0.7638        | 8.0   | 26   | 0.7570          | 0.6731   |
| 0.7638        | 8.92  | 29   | 0.7214          | 0.6923   |
| 0.6277        | 9.23  | 30   | 0.7292          | 0.6538   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2