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_07
+  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: 0.9094533029612756
+    - name: Precision
+      type: precision
+      value: 0.9188664294996836
+---
+
+<!-- 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_07
+
+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: 0.2904
+- Accuracy: 0.9095
+- F1 Score: 0.9095
+- Precision: 0.9189
+
+## 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: 5e-05
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|
+| 1.3335        | 0.98  | 13   | 0.9195          | 0.6281   | 0.6111   | 0.7245    |
+| 0.6062        | 1.96  | 26   | 0.6114          | 0.7625   | 0.7673   | 0.8385    |
+| 0.274         | 2.94  | 39   | 0.5468          | 0.7802   | 0.7772   | 0.8533    |
+| 0.1211        | 4.0   | 53   | 0.3922          | 0.8417   | 0.8417   | 0.8749    |
+| 0.0991        | 4.98  | 66   | 0.4734          | 0.8172   | 0.8209   | 0.8802    |
+| 0.0682        | 5.96  | 79   | 0.3751          | 0.8599   | 0.8600   | 0.8882    |
+| 0.0414        | 6.94  | 92   | 0.2951          | 0.8986   | 0.8995   | 0.9100    |
+| 0.0264        | 8.0   | 106  | 0.3485          | 0.8844   | 0.8855   | 0.9069    |
+| 0.021         | 8.98  | 119  | 0.3803          | 0.8764   | 0.8782   | 0.9031    |
+| 0.0151        | 9.81  | 130  | 0.2904          | 0.9095   | 0.9095   | 0.9189    |
+
+
+### Framework versions
+
+- Transformers 4.29.2
+- Pytorch 2.0.1+cu117
+- Datasets 2.12.0
+- Tokenizers 0.13.3