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---
license: apache-2.0
base_model: microsoft/swin-large-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Psoriasis-500-100aug-224-swinv2-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: 0.8227074235807861
---
<!-- 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-large
This model is a fine-tuned version of [microsoft/swin-large-patch4-window7-224](https://huggingface.co/microsoft/swin-large-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7383
- Accuracy: 0.8227
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.4126 | 0.9840 | 46 | 0.9408 | 0.6882 |
| 0.3672 | 1.9893 | 93 | 0.6431 | 0.7703 |
| 0.133 | 2.9947 | 140 | 0.5938 | 0.7921 |
| 0.0624 | 4.0 | 187 | 0.6128 | 0.8035 |
| 0.0473 | 4.9840 | 233 | 0.6654 | 0.8114 |
| 0.0276 | 5.9893 | 280 | 0.7090 | 0.8166 |
| 0.0111 | 6.9947 | 327 | 0.7133 | 0.8140 |
| 0.0081 | 8.0 | 374 | 0.7639 | 0.8183 |
| 0.0039 | 8.9840 | 420 | 0.7387 | 0.8236 |
| 0.0065 | 9.8396 | 460 | 0.7383 | 0.8227 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
|