---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: beit-base-patch16-224-pt22k-ft22k-finetunedt
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: train
      split: train
      args: train
    metrics:
    - name: Accuracy
      type: accuracy
      value: 1.0
---

<!-- 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. -->

# beit-base-patch16-224-pt22k-ft22k-finetunedt

This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0147
- Accuracy: 1.0

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4714        | 1.0   | 25   | 0.0147          | 1.0      |
| 0.0089        | 2.0   | 50   | 0.0008          | 1.0      |
| 0.0101        | 3.0   | 75   | 0.0003          | 1.0      |
| 0.0021        | 4.0   | 100  | 0.0002          | 1.0      |
| 0.0028        | 5.0   | 125  | 0.0001          | 1.0      |
| 0.0016        | 6.0   | 150  | 0.0001          | 1.0      |
| 0.0044        | 7.0   | 175  | 0.0001          | 1.0      |
| 0.0007        | 8.0   | 200  | 0.0001          | 1.0      |
| 0.0013        | 9.0   | 225  | 0.0001          | 1.0      |
| 0.0004        | 10.0  | 250  | 0.0001          | 1.0      |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2