---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: test-trainer
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: Chess
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9107142857142857
    - name: F1
      type: f1
      value: 0.9121670865142396
    - name: Precision
      type: precision
      value: 0.9171626984126985
    - name: Recall
      type: recall
      value: 0.9107142857142857
---

<!-- 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. -->

# test-trainer

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 Chess dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7291
- Accuracy: 0.9107
- F1: 0.9122
- Precision: 0.9172
- Recall: 0.9107

## 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: 2e-05
- train_batch_size: 10
- eval_batch_size: 4
- seed: 42
- optimizer: Use 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 1.0   | 50   | 1.6720          | 0.4821   | 0.4134 | 0.3870    | 0.4821 |
| No log        | 2.0   | 100  | 1.4652          | 0.6429   | 0.6126 | 0.7414    | 0.6429 |
| No log        | 3.0   | 150  | 1.1742          | 0.7321   | 0.7210 | 0.7792    | 0.7321 |
| No log        | 4.0   | 200  | 0.9813          | 0.8393   | 0.8433 | 0.8589    | 0.8393 |
| No log        | 5.0   | 250  | 0.8312          | 0.8214   | 0.8164 | 0.8516    | 0.8214 |
| No log        | 6.0   | 300  | 0.7291          | 0.9107   | 0.9122 | 0.9172    | 0.9107 |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.2.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3