Model save

README.md

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+---
+license: apache-2.0
+library_name: peft
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+base_model: microsoft/swin-large-patch4-window7-224-in22k
+model-index:
+- name: chest-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. -->
+
+# chest-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 an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1064
+- Accuracy: 0.9622
+- Precision: 0.9377
+- Recall: 0.9723
+- F1: 0.9530
+
+## 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     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 0.3305        | 0.99  | 63   | 0.1600          | 0.9365   | 0.9478    | 0.8868 | 0.9119 |
+| 0.2335        | 1.99  | 127  | 0.1552          | 0.9313   | 0.8968    | 0.9472 | 0.9166 |
+| 0.1977        | 3.0   | 191  | 0.0855          | 0.9734   | 0.9608    | 0.9714 | 0.9659 |
+| 0.1746        | 4.0   | 255  | 0.0870          | 0.9794   | 0.9794    | 0.9669 | 0.9729 |
+| 0.1797        | 4.99  | 318  | 0.0829          | 0.9700   | 0.9549    | 0.9690 | 0.9617 |
+| 0.1436        | 5.99  | 382  | 0.0797          | 0.9708   | 0.9556    | 0.9707 | 0.9628 |
+| 0.1632        | 7.0   | 446  | 0.0816          | 0.9700   | 0.9508    | 0.9754 | 0.9621 |
+| 0.1125        | 8.0   | 510  | 0.1007          | 0.9614   | 0.9365    | 0.9717 | 0.9519 |
+| 0.1076        | 8.99  | 573  | 0.0900          | 0.9691   | 0.9482    | 0.9770 | 0.9612 |
+| 0.1188        | 9.88  | 630  | 0.1064          | 0.9622   | 0.9377    | 0.9723 | 0.9530 |
+
+
+### 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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