---
license: apache-2.0
base_model: microsoft/beit-large-patch16-224-pt22k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Psoriasis-500-100aug-224-beit-large
  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: 0.7991266375545851
---

<!-- 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. -->

# Psoriasis-500-100aug-224-beit-large

This model is a fine-tuned version of [microsoft/beit-large-patch16-224-pt22k](https://huggingface.co/microsoft/beit-large-patch16-224-pt22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1823
- Accuracy: 0.7991

## 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: 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: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.8236        | 0.9973 | 92   | 1.1536          | 0.6358   |
| 0.4282        | 1.9946 | 184  | 0.8848          | 0.7389   |
| 0.2305        | 2.9919 | 276  | 0.9811          | 0.7258   |
| 0.1206        | 4.0    | 369  | 0.8858          | 0.7808   |
| 0.1107        | 4.9973 | 461  | 1.1129          | 0.7397   |
| 0.0319        | 5.9946 | 553  | 1.1625          | 0.7703   |
| 0.0073        | 6.9919 | 645  | 1.1938          | 0.7895   |
| 0.0078        | 8.0    | 738  | 1.3031          | 0.7790   |
| 0.0013        | 8.9973 | 830  | 1.2117          | 0.7974   |
| 0.002         | 9.9729 | 920  | 1.1823          | 0.7991   |

# Classification Report

| Class               | Precision (%) | Recall (%) | F1-Score (%) | Support |
|---------------------|---------------|------------|--------------|---------|
| Abnormal            | 66            | 62         | 64           | 108     |
| Erythrodermic       | 96            | 76         | 85           | 100     |
| Guttate             | 95            | 83         | 89           | 114     |
| Inverse             | 83            | 91         | 87           | 108     |
| Nail                | 81            | 84         | 83           | 99      |
| Normal              | 81            | 79         | 80           | 82      |
| Not Define          | 98            | 95         | 96           | 92      |
| Palm Soles          | 82            | 88         | 85           | 102     |
| Plaque              | 70            | 88         | 78           | 84      |
| Psoriatic Arthritis | 78            | 74         | 76           | 104     |
| Pustular            | 71            | 76         | 74           | 112     |
| Scalp               | 84            | 86         | 85           | 80      |
| **Accuracy**        |               |            | **82**       | 1185    |
| **Macro Avg**       | **82**        | **82**     | **82**       | 1185    |
| **Weighted Avg**    | **82**        | **82**     | **82**       | 1185    |

---

### Framework versions

- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1