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: finalProject
+  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: 0.9886095836606441
+    - name: Precision
+      type: precision
+      value: 0.9890037194336745
+---
+
+<!-- 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. -->
+
+# finalProject
+
+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: 0.0376
+- Accuracy: 0.9886
+- F1 Score: 0.9888
+- Precision: 0.9890
+- Sensitivity: 0.9887
+- Specificity: 0.9971
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:-----------:|:-----------:|
+| 0.3384        | 1.0   | 30   | 0.2387          | 0.9144   | 0.9163   | 0.9197    | 0.9146      | 0.9781      |
+| 0.1608        | 2.0   | 60   | 0.1635          | 0.9466   | 0.9476   | 0.9485    | 0.9474      | 0.9865      |
+| 0.0953        | 3.0   | 90   | 0.0915          | 0.9698   | 0.9703   | 0.9706    | 0.9706      | 0.9924      |
+| 0.0573        | 4.0   | 120  | 0.1125          | 0.9607   | 0.9617   | 0.9634    | 0.9621      | 0.9901      |
+| 0.0335        | 5.0   | 150  | 0.0536          | 0.9827   | 0.9831   | 0.9837    | 0.9826      | 0.9957      |
+| 0.0185        | 6.0   | 180  | 0.0543          | 0.9827   | 0.9830   | 0.9837    | 0.9825      | 0.9957      |
+| 0.0226        | 7.0   | 210  | 0.0478          | 0.9859   | 0.9861   | 0.9866    | 0.9856      | 0.9965      |
+| 0.0131        | 8.0   | 240  | 0.0468          | 0.9843   | 0.9846   | 0.9847    | 0.9846      | 0.9961      |
+| 0.0087        | 9.0   | 270  | 0.0411          | 0.9890   | 0.9892   | 0.9894    | 0.9891      | 0.9972      |
+| 0.0043        | 10.0  | 300  | 0.0376          | 0.9886   | 0.9888   | 0.9890    | 0.9887      | 0.9971      |
+
+
+### Framework versions
+
+- Transformers 4.30.2
+- Pytorch 2.0.1+cu117
+- Datasets 2.13.0
+- Tokenizers 0.13.3