update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- precision
+model-index:
+- name: swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_12
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9736842105263158
+    - name: Precision
+      type: precision
+      value: 0.973953427126813
+---
+
+<!-- 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. -->
+
+# swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_12
+
+This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0923
+- Accuracy: 0.9737
+- F1 Score: 0.9737
+- Precision: 0.9740
+- Sensitivity: 0.9738
+- Specificity: 0.9934
+
+## 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: 0.0001
+- train_batch_size: 64
+- eval_batch_size: 64
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 256
+- 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 | F1 Score | Precision | Sensitivity | Specificity |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:-----------:|:-----------:|
+| 0.3644        | 1.0   | 30   | 0.2918          | 0.8955   | 0.8974   | 0.9070    | 0.8957      | 0.9734      |
+| 0.2177        | 2.0   | 60   | 0.2319          | 0.9152   | 0.9155   | 0.9237    | 0.9156      | 0.9786      |
+| 0.1171        | 3.0   | 90   | 0.1654          | 0.9489   | 0.9494   | 0.9532    | 0.9492      | 0.9872      |
+| 0.068         | 4.0   | 120  | 0.1600          | 0.9450   | 0.9451   | 0.9466    | 0.9455      | 0.9861      |
+| 0.0499        | 5.0   | 150  | 0.0947          | 0.9654   | 0.9656   | 0.9656    | 0.9657      | 0.9913      |
+| 0.0302        | 6.0   | 180  | 0.0882          | 0.9713   | 0.9714   | 0.9715    | 0.9715      | 0.9928      |
+| 0.0207        | 7.0   | 210  | 0.1002          | 0.9698   | 0.9699   | 0.9708    | 0.9699      | 0.9924      |
+| 0.0205        | 8.0   | 240  | 0.1550          | 0.9525   | 0.9521   | 0.9544    | 0.9529      | 0.9881      |
+| 0.0163        | 9.0   | 270  | 0.0789          | 0.9760   | 0.9761   | 0.9762    | 0.9762      | 0.9940      |
+| 0.0181        | 10.0  | 300  | 0.0923          | 0.9737   | 0.9737   | 0.9740    | 0.9738      | 0.9934      |
+
+
+### Framework versions
+
+- Transformers 4.29.2
+- Pytorch 2.0.1+cu117
+- Datasets 2.12.0
+- Tokenizers 0.13.3