---
license: apache-2.0
base_model: motheecreator/vit-base-patch16-224-in21k-finetuned
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-finetuned
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: image_folder
      type: image_folder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8571428571428571
---

<!-- 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-base-patch16-224-in21k-finetuned

This model is a fine-tuned version of [motheecreator/vit-base-patch16-224-in21k-finetuned](https://huggingface.co/motheecreator/vit-base-patch16-224-in21k-finetuned) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4353
- Accuracy: 0.8571

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 0.7964        | 1.0   | 798  | 0.7271   | 0.7869          |
| 0.6567        | 2.0   | 1596 | 0.7380   | 0.7539          |
| 0.6842        | 3.0   | 2394 | 0.7837   | 0.6287          |
| 0.5242        | 4.0   | 3192 | 0.7839   | 0.6282          |
| 0.4321        | 5.0   | 3990 | 0.7823   | 0.6423          |
| 0.3129        | 6.0   | 4788 | 0.7838   | 0.6533          |
| 0.4245        | 7.0   | 5586 | 0.4382   | 0.8542          |
| 0.3806        | 8.0   | 6384 | 0.4375   | 0.8531          |
| 0.3112        | 9.0   | 7182 | 0.4372   | 0.8557          |
| 0.2692        | 10.0  | 7980 | 0.4353   | 0.8571          |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0