---
library_name: peft
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- base_model:adapter:google/vit-base-patch16-224-in21k
- lora
- transformers
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-finetuned-chessman2
  results:
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - type: accuracy
      value: 0.9636363636363636
      name: Accuracy
---

<!-- 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. -->

# vit-finetuned-chessman2

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: 0.1620
- Accuracy: 0.9636

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5954        | 1.0   | 28   | 1.3698          | 0.6545   |
| 0.9802        | 2.0   | 56   | 0.7982          | 0.8      |
| 0.4706        | 3.0   | 84   | 0.4171          | 0.9455   |
| 0.2342        | 4.0   | 112  | 0.2657          | 0.9455   |
| 0.1416        | 5.0   | 140  | 0.2017          | 0.9636   |
| 0.1059        | 6.0   | 168  | 0.1813          | 0.9636   |
| 0.0872        | 7.0   | 196  | 0.1655          | 0.9636   |
| 0.0749        | 8.0   | 224  | 0.1620          | 0.9636   |


### Framework versions

- PEFT 0.19.1
- Transformers 4.57.6
- Pytorch 2.10.0
- Datasets 5.0.0
- Tokenizers 0.22.2