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_11
+  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: 0.8566378633150039
+    - name: Precision
+      type: precision
+      value: 0.8916086530475995
+---
+
+<!-- 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_11
+
+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: 0.5327
+- Accuracy: 0.8566
+- F1 Score: 0.8626
+- Precision: 0.8916
+- Sensitivity: 0.8582
+- Specificity: 0.9636
+
+## 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: 0.001
+- train_batch_size: 100
+- eval_batch_size: 100
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 400
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:-----------:|:-----------:|
+| 1.2738        | 0.99  | 19   | 3.6805          | 0.3606   | 0.2878   | 0.6974    | 0.3569      | 0.8381      |
+| 0.8184        | 1.97  | 38   | 2.2141          | 0.5196   | 0.4842   | 0.8085    | 0.5311      | 0.8793      |
+| 0.3966        | 2.96  | 57   | 0.6808          | 0.8009   | 0.7976   | 0.8463    | 0.8029      | 0.9491      |
+| 0.2336        | 4.0   | 77   | 0.5914          | 0.7946   | 0.8011   | 0.8170    | 0.7978      | 0.9461      |
+| 0.1196        | 4.99  | 96   | 0.4851          | 0.8606   | 0.8606   | 0.8779    | 0.8645      | 0.9644      |
+| 0.1003        | 5.97  | 115  | 0.5353          | 0.8413   | 0.8475   | 0.8716    | 0.8442      | 0.9593      |
+| 0.0876        | 6.96  | 134  | 0.6691          | 0.8032   | 0.8151   | 0.8766    | 0.8051      | 0.9503      |
+| 0.0569        | 8.0   | 154  | 0.4531          | 0.8570   | 0.8610   | 0.8776    | 0.8614      | 0.9634      |
+| 0.0329        | 8.99  | 173  | 0.8581          | 0.7977   | 0.8081   | 0.8692    | 0.8023      | 0.9488      |
+| 0.0262        | 9.87  | 190  | 0.5327          | 0.8566   | 0.8626   | 0.8916    | 0.8582      | 0.9636      |
+
+
+### Framework versions
+
+- Transformers 4.29.2
+- Pytorch 2.0.1+cu117
+- Datasets 2.12.0
+- Tokenizers 0.13.3