Model save

README.md

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+---
+license: apache-2.0
+library_name: peft
+tags:
+- generated_from_trainer
+base_model: microsoft/swin-large-patch4-window7-224-in22k
+datasets:
+- medmnist-v2
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: breastmnist-swin-base-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. -->
+
+# breastmnist-swin-base-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: 0.3214
+- Accuracy: 0.8846
+- Precision: 0.8506
+- Recall: 0.8609
+- F1: 0.8555
+
+## 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     |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| No log        | 0.9143 | 8    | 0.5069          | 0.7436   | 0.8701    | 0.5238 | 0.4708 |
+| 0.6976        | 1.9429 | 17   | 0.4591          | 0.8590   | 0.8190    | 0.8283 | 0.8234 |
+| 0.5351        | 2.9714 | 26   | 0.3745          | 0.8846   | 0.8667    | 0.8308 | 0.8462 |
+| 0.4998        | 4.0    | 35   | 0.3243          | 0.8974   | 0.8697    | 0.8697 | 0.8697 |
+| 0.4569        | 4.9143 | 43   | 0.4070          | 0.8590   | 0.8306    | 0.7982 | 0.8120 |
+| 0.4182        | 5.9429 | 52   | 0.3801          | 0.8718   | 0.8439    | 0.8221 | 0.8319 |
+| 0.4432        | 6.9714 | 61   | 0.3071          | 0.8718   | 0.8371    | 0.8371 | 0.8371 |
+| 0.3988        | 8.0    | 70   | 0.3205          | 0.8718   | 0.8332    | 0.8521 | 0.8417 |
+| 0.3988        | 8.9143 | 78   | 0.3239          | 0.8846   | 0.8506    | 0.8609 | 0.8555 |
+| 0.3993        | 9.1429 | 80   | 0.3214          | 0.8846   | 0.8506    | 0.8609 | 0.8555 |
+
+
+### Framework versions
+
+- PEFT 0.11.1
+- Transformers 4.41.1
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

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