---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: beit-base-patch16-224-OT-3
  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: 0.8709677419354839
---

<!-- 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-OT-3

This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co./microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3932
- Accuracy: 0.8710

## 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: 4e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.91  | 5    | 1.3788          | 0.5      |
| 1.3775        | 2.0   | 11   | 1.3397          | 0.5161   |
| 1.3775        | 2.91  | 16   | 1.2780          | 0.5161   |
| 1.2993        | 4.0   | 22   | 1.1642          | 0.6774   |
| 1.2993        | 4.91  | 27   | 1.0752          | 0.7097   |
| 1.1713        | 6.0   | 33   | 0.9749          | 0.7258   |
| 1.1713        | 6.91  | 38   | 0.8666          | 0.7581   |
| 0.9956        | 8.0   | 44   | 0.7634          | 0.8065   |
| 0.9956        | 8.91  | 49   | 0.6863          | 0.8226   |
| 0.845         | 10.0  | 55   | 0.6005          | 0.8226   |
| 0.7145        | 10.91 | 60   | 0.5364          | 0.8387   |
| 0.7145        | 12.0  | 66   | 0.5585          | 0.8065   |
| 0.5907        | 12.91 | 71   | 0.4962          | 0.7742   |
| 0.5907        | 14.0  | 77   | 0.5070          | 0.7581   |
| 0.5531        | 14.91 | 82   | 0.4648          | 0.8226   |
| 0.5531        | 16.0  | 88   | 0.4812          | 0.7581   |
| 0.4878        | 16.91 | 93   | 0.4281          | 0.8226   |
| 0.4878        | 18.0  | 99   | 0.4796          | 0.7419   |
| 0.4583        | 18.91 | 104  | 0.3913          | 0.8226   |
| 0.4546        | 20.0  | 110  | 0.4085          | 0.7742   |
| 0.4546        | 20.91 | 115  | 0.4016          | 0.8387   |
| 0.4118        | 22.0  | 121  | 0.4125          | 0.8226   |
| 0.4118        | 22.91 | 126  | 0.4282          | 0.8226   |
| 0.3939        | 24.0  | 132  | 0.4869          | 0.7742   |
| 0.3939        | 24.91 | 137  | 0.3723          | 0.8548   |
| 0.4138        | 26.0  | 143  | 0.4032          | 0.8065   |
| 0.4138        | 26.91 | 148  | 0.4397          | 0.8065   |
| 0.3599        | 28.0  | 154  | 0.3714          | 0.8548   |
| 0.3599        | 28.91 | 159  | 0.3800          | 0.8548   |
| 0.3629        | 30.0  | 165  | 0.4158          | 0.8065   |
| 0.336         | 30.91 | 170  | 0.4100          | 0.8226   |
| 0.336         | 32.0  | 176  | 0.4001          | 0.8387   |
| 0.3306        | 32.91 | 181  | 0.3925          | 0.8548   |
| 0.3306        | 34.0  | 187  | 0.3932          | 0.8710   |
| 0.3319        | 34.91 | 192  | 0.3942          | 0.8710   |
| 0.3319        | 36.0  | 198  | 0.3883          | 0.8710   |
| 0.3324        | 36.36 | 200  | 0.3886          | 0.8710   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0