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---
license: apache-2.0
base_model: microsoft/swinv2-base-patch4-window12-192-22k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Psoriasis-500-100aug-224-swinv2-base-patch4-window12-192-22k
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: 0.8200873362445414
---
<!-- 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-swinv2-base-patch4-window12-192-22k
This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window12-192-22k](https://huggingface.co/microsoft/swinv2-base-patch4-window12-192-22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1589
- Accuracy: 0.8201
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.5248 | 0.9973 | 92 | 0.7503 | 0.7694 |
| 0.2461 | 1.9946 | 184 | 0.8202 | 0.7764 |
| 0.1164 | 2.9919 | 276 | 0.8260 | 0.8052 |
| 0.0656 | 4.0 | 369 | 1.0366 | 0.7860 |
| 0.0525 | 4.9973 | 461 | 1.0025 | 0.8148 |
| 0.0223 | 5.9946 | 553 | 1.1363 | 0.7965 |
| 0.0022 | 6.9919 | 645 | 1.1911 | 0.8061 |
| 0.009 | 8.0 | 738 | 1.2139 | 0.7965 |
| 0.0073 | 8.9973 | 830 | 1.2066 | 0.8166 |
| 0.0014 | 9.9729 | 920 | 1.1589 | 0.8201 |
# Classification Report
| Class | Precision (%) | Recall (%) | F1-Score (%) | Support |
|---------------------|---------------|------------|--------------|---------|
| Abnormal | 68 | 67 | 67 | 108 |
| Erythrodermic | 99 | 75 | 85 | 100 |
| Guttate | 94 | 84 | 89 | 114 |
| Inverse | 88 | 93 | 90 | 108 |
| Nail | 88 | 86 | 87 | 99 |
| Normal | 84 | 87 | 85 | 82 |
| Not Define | 98 | 99 | 98 | 92 |
| Palm Soles | 80 | 80 | 80 | 102 |
| Plaque | 73 | 92 | 81 | 84 |
| Psoriatic Arthritis | 88 | 75 | 81 | 104 |
| Pustular | 76 | 86 | 80 | 112 |
| Scalp | 86 | 94 | 90 | 80 |
| **Accuracy** | | | **84** | 1185 |
| **Macro Avg** | **85** | **85** | **84** | 1185 |
| **Weighted Avg** | **85** | **84** | **84** | 1185 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
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