---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
model-index:
- name: vit-base-patch16-224
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8033333333333333
    - name: Precision
      type: precision
      value: 0.7988653846153846
    - name: Recall
      type: recall
      value: 0.8033333333333333
---

<!-- 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

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co./google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4775
- Accuracy: 0.8033
- Precision: 0.7989
- Recall: 0.8033
- F1 Score: 0.7784

## 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 | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| No log        | 1.0   | 8    | 0.5941          | 0.7333   | 0.5378    | 0.7333 | 0.6205   |
| 0.6385        | 2.0   | 16   | 0.5391          | 0.775    | 0.7830    | 0.775  | 0.7210   |
| 0.546         | 3.0   | 24   | 0.5417          | 0.775    | 0.7658    | 0.775  | 0.7321   |
| 0.481         | 4.0   | 32   | 0.5486          | 0.7833   | 0.8030    | 0.7833 | 0.7313   |
| 0.3841        | 5.0   | 40   | 0.5420          | 0.7875   | 0.7825    | 0.7875 | 0.7515   |
| 0.3841        | 6.0   | 48   | 0.5246          | 0.8292   | 0.8358    | 0.8292 | 0.8068   |
| 0.2565        | 7.0   | 56   | 0.5763          | 0.8083   | 0.8070    | 0.8083 | 0.7821   |
| 0.1605        | 8.0   | 64   | 0.5433          | 0.825    | 0.8180    | 0.825  | 0.8120   |
| 0.0824        | 9.0   | 72   | 0.6010          | 0.8125   | 0.8027    | 0.8125 | 0.7994   |
| 0.0489        | 10.0  | 80   | 0.6063          | 0.8125   | 0.8032    | 0.8125 | 0.7977   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3