---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: chessdata-model
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train[:5000]
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8378378378378378
---

<!-- 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. -->

# chessdata-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: 0.5827
- Accuracy: 0.8378

## 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   | 7    | 1.1069          | 0.7207   |
| 1.0143        | 2.0   | 14   | 1.0853          | 0.7117   |
| 0.9148        | 3.0   | 21   | 0.9472          | 0.7297   |
| 0.9148        | 4.0   | 28   | 0.8859          | 0.7568   |
| 0.7721        | 5.0   | 35   | 0.8500          | 0.7658   |
| 0.71          | 6.0   | 42   | 0.7973          | 0.8108   |
| 0.71          | 7.0   | 49   | 0.8040          | 0.7748   |
| 0.641         | 8.0   | 56   | 0.8344          | 0.7207   |
| 0.6122        | 9.0   | 63   | 0.7528          | 0.7748   |
| 0.5698        | 10.0  | 70   | 0.8087          | 0.7748   |
| 0.5698        | 11.0  | 77   | 0.7347          | 0.7838   |
| 0.5329        | 12.0  | 84   | 0.6237          | 0.8288   |
| 0.5264        | 13.0  | 91   | 0.6135          | 0.8378   |
| 0.5264        | 14.0  | 98   | 0.7670          | 0.7568   |
| 0.4846        | 15.0  | 105  | 0.6465          | 0.8288   |
| 0.4597        | 16.0  | 112  | 0.6354          | 0.8288   |
| 0.4597        | 17.0  | 119  | 0.7096          | 0.7838   |
| 0.409         | 18.0  | 126  | 0.6364          | 0.8468   |
| 0.4321        | 19.0  | 133  | 0.6343          | 0.8108   |
| 0.4309        | 20.0  | 140  | 0.5827          | 0.8378   |


### Framework versions

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