Model save

README.md

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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: 0.5665
+- Accuracy: 0.7856
+- Precision: 0.6470
+- Recall: 0.6288
+- F1: 0.6355
+
+## 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

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