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---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
datasets:
- medmnist-v2
metrics:
- accuracy
- precision
- recall
- f1
base_model: microsoft/swin-large-patch4-window7-224-in22k
model-index:
- name: derma-swin-large-finetuned
results: []
---
<!-- 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. -->
# derma-swin-large-finetuned
This model is a fine-tuned version of [microsoft/swin-large-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-large-patch4-window7-224-in22k) on the medmnist-v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5530
- Accuracy: 0.7875
- Precision: 0.6308
- Recall: 0.6101
- F1: 0.6150
## 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: 0.005
- 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
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.8516 | 1.0 | 109 | 0.7470 | 0.7547 | 0.5456 | 0.3914 | 0.4188 |
| 0.7738 | 2.0 | 219 | 0.8953 | 0.7168 | 0.4225 | 0.4459 | 0.3577 |
| 0.6994 | 3.0 | 328 | 0.6593 | 0.7607 | 0.6257 | 0.5059 | 0.5105 |
| 0.6731 | 4.0 | 438 | 0.6145 | 0.7717 | 0.6322 | 0.5001 | 0.5383 |
| 0.7266 | 5.0 | 547 | 0.6839 | 0.7398 | 0.5520 | 0.5344 | 0.4935 |
| 0.6388 | 6.0 | 657 | 0.6243 | 0.7667 | 0.6117 | 0.5063 | 0.5338 |
| 0.6495 | 7.0 | 766 | 0.6161 | 0.7827 | 0.6357 | 0.6153 | 0.6163 |
| 0.5639 | 8.0 | 876 | 0.5752 | 0.7836 | 0.6018 | 0.5912 | 0.5931 |
| 0.6012 | 9.0 | 985 | 0.5508 | 0.7926 | 0.6303 | 0.6195 | 0.6176 |
| 0.5468 | 9.95 | 1090 | 0.5665 | 0.7856 | 0.6470 | 0.6288 | 0.6355 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |