---
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: 0.7833333333333333
    - name: Precision
      type: precision
      value: 0.7701923076923076
    - name: Recall
      type: recall
      value: 0.7833333333333333
---

<!-- 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: 0.4772
- Accuracy: 0.7833
- Precision: 0.7702
- Recall: 0.7833
- F1 Score: 0.7559

## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| No log        | 1.0   | 4    | 0.6010          | 0.7333   | 0.6725    | 0.7333 | 0.6280   |
| No log        | 2.0   | 8    | 0.5552          | 0.7375   | 0.8067    | 0.7375 | 0.6302   |
| No log        | 3.0   | 12   | 0.5450          | 0.7542   | 0.7598    | 0.7542 | 0.6782   |
| 0.576         | 4.0   | 16   | 0.5325          | 0.75     | 0.7707    | 0.75   | 0.6641   |
| 0.576         | 5.0   | 20   | 0.5234          | 0.75     | 0.7232    | 0.75   | 0.6900   |
| 0.576         | 6.0   | 24   | 0.5112          | 0.7625   | 0.7506    | 0.7625 | 0.7076   |
| 0.576         | 7.0   | 28   | 0.5082          | 0.7667   | 0.7503    | 0.7667 | 0.7221   |
| 0.4876        | 8.0   | 32   | 0.5067          | 0.7667   | 0.7466    | 0.7667 | 0.7288   |
| 0.4876        | 9.0   | 36   | 0.5091          | 0.7792   | 0.7623    | 0.7792 | 0.7528   |
| 0.4876        | 10.0  | 40   | 0.5023          | 0.7583   | 0.7393    | 0.7583 | 0.7045   |
| 0.4876        | 11.0  | 44   | 0.4911          | 0.7708   | 0.7507    | 0.7708 | 0.7435   |
| 0.4379        | 12.0  | 48   | 0.4921          | 0.7667   | 0.7487    | 0.7667 | 0.7513   |
| 0.4379        | 13.0  | 52   | 0.4906          | 0.7917   | 0.7792    | 0.7917 | 0.7680   |
| 0.4379        | 14.0  | 56   | 0.4919          | 0.7875   | 0.7731    | 0.7875 | 0.7645   |
| 0.4003        | 15.0  | 60   | 0.4929          | 0.7833   | 0.7678    | 0.7833 | 0.7587   |


### Framework versions

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