---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Chess_Images
  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: 0.9
---

<!-- 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_Images

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: 0.5284
- Accuracy: 0.9

## 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 2    | 1.0120          | 0.7      |
| No log        | 2.0   | 4    | 0.9958          | 0.8      |
| No log        | 3.0   | 6    | 0.9576          | 0.8333   |
| No log        | 4.0   | 8    | 0.8673          | 0.8333   |
| 0.8292        | 5.0   | 10   | 0.8140          | 0.8667   |
| 0.8292        | 6.0   | 12   | 0.7034          | 0.9      |
| 0.8292        | 7.0   | 14   | 0.7036          | 0.9      |
| 0.8292        | 8.0   | 16   | 0.6949          | 0.9333   |
| 0.8292        | 9.0   | 18   | 0.5620          | 0.9667   |
| 0.6112        | 10.0  | 20   | 0.5829          | 0.9333   |
| 0.6112        | 11.0  | 22   | 0.6530          | 0.9      |
| 0.6112        | 12.0  | 24   | 0.5664          | 0.9333   |
| 0.6112        | 13.0  | 26   | 0.5084          | 1.0      |
| 0.6112        | 14.0  | 28   | 0.6490          | 0.8333   |
| 0.4805        | 15.0  | 30   | 0.4700          | 1.0      |
| 0.4805        | 16.0  | 32   | 0.5473          | 0.9333   |
| 0.4805        | 17.0  | 34   | 0.4928          | 0.9667   |
| 0.4805        | 18.0  | 36   | 0.5023          | 0.9667   |
| 0.4805        | 19.0  | 38   | 0.4885          | 0.9333   |
| 0.4145        | 20.0  | 40   | 0.5284          | 0.9      |


### Framework versions

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