---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-pretraining-2024_03_25-classifier
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7648975791433892
---

<!-- 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-pretraining-2024_03_25-classifier

This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5083
- Accuracy: 0.7649

## 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-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6422        | 1.0   | 537   | 0.6409          | 0.6560   |
| 0.5509        | 2.0   | 1074  | 0.5966          | 0.6862   |
| 0.5123        | 3.0   | 1611  | 0.5743          | 0.7044   |
| 0.5237        | 4.0   | 2148  | 0.5523          | 0.7188   |
| 0.5589        | 5.0   | 2685  | 0.5352          | 0.7370   |
| 0.5671        | 6.0   | 3222  | 0.5317          | 0.7407   |
| 0.5247        | 7.0   | 3759  | 0.5228          | 0.7486   |
| 0.4855        | 8.0   | 4296  | 0.5422          | 0.7374   |
| 0.5122        | 9.0   | 4833  | 0.5195          | 0.7477   |
| 0.5381        | 10.0  | 5370  | 0.5277          | 0.7398   |
| 0.5465        | 11.0  | 5907  | 0.5213          | 0.7514   |
| 0.4552        | 12.0  | 6444  | 0.5300          | 0.7495   |
| 0.5188        | 13.0  | 6981  | 0.5107          | 0.7505   |
| 0.5056        | 14.0  | 7518  | 0.5075          | 0.7579   |
| 0.4759        | 15.0  | 8055  | 0.5077          | 0.7644   |
| 0.6042        | 16.0  | 8592  | 0.5143          | 0.7602   |
| 0.4002        | 17.0  | 9129  | 0.5184          | 0.7612   |
| 0.4664        | 18.0  | 9666  | 0.5072          | 0.7630   |
| 0.4653        | 19.0  | 10203 | 0.5103          | 0.7626   |
| 0.4096        | 20.0  | 10740 | 0.5083          | 0.7649   |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2