---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
model-index:
- name: beit-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: 0.7333333333333333
    - name: Precision
      type: precision
      value: 0.708216298040535
    - name: Recall
      type: recall
      value: 0.7333333333333333
---

<!-- 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

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: 0.5490
- Accuracy: 0.7333
- Precision: 0.7082
- Recall: 0.7333
- F1 Score: 0.7050

## 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    | 0.6369          | 0.725    | 0.5256    | 0.725  | 0.6094   |
| No log        | 2.0   | 8    | 0.6192          | 0.7458   | 0.7215    | 0.7458 | 0.6907   |
| No log        | 3.0   | 12   | 0.5699          | 0.725    | 0.5256    | 0.725  | 0.6094   |
| 0.727         | 4.0   | 16   | 0.6237          | 0.6792   | 0.6716    | 0.6792 | 0.6751   |
| 0.727         | 5.0   | 20   | 0.5533          | 0.7292   | 0.8028    | 0.7292 | 0.6191   |
| 0.727         | 6.0   | 24   | 0.5601          | 0.7375   | 0.7200    | 0.7375 | 0.6562   |
| 0.727         | 7.0   | 28   | 0.5901          | 0.7167   | 0.6944    | 0.7167 | 0.7013   |
| 0.5968        | 8.0   | 32   | 0.5543          | 0.7375   | 0.7081    | 0.7375 | 0.7080   |
| 0.5968        | 9.0   | 36   | 0.5780          | 0.7208   | 0.7095    | 0.7208 | 0.7141   |
| 0.5968        | 10.0  | 40   | 0.5389          | 0.7375   | 0.7049    | 0.7375 | 0.6990   |
| 0.5968        | 11.0  | 44   | 0.5438          | 0.7542   | 0.7306    | 0.7542 | 0.7238   |
| 0.5631        | 12.0  | 48   | 0.5426          | 0.7458   | 0.7187    | 0.7458 | 0.7145   |
| 0.5631        | 13.0  | 52   | 0.5383          | 0.7458   | 0.7187    | 0.7458 | 0.7145   |
| 0.5631        | 14.0  | 56   | 0.5432          | 0.7458   | 0.7239    | 0.7458 | 0.7269   |
| 0.541         | 15.0  | 60   | 0.5453          | 0.7417   | 0.7212    | 0.7417 | 0.7256   |


### Framework versions

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