Model save

README.md

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+---
+license: apache-2.0
+base_model: google/vit-base-patch16-224-in21k
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- f1
+- recall
+- precision
+model-index:
+- name: vit-base-patch16-224-in21k_covid_19_ct_scans
+  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: 0.8466666666666667
+    - name: F1
+      type: f1
+      value: 0.8571428571428571
+    - name: Recall
+      type: recall
+      value: 0.8625
+    - name: Precision
+      type: precision
+      value: 0.8518518518518519
+---
+
+<!-- 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. -->
+
+# vit-base-patch16-224-in21k_covid_19_ct_scans
+
+This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3062
+- Accuracy: 0.8467
+- F1: 0.8571
+- Recall: 0.8625
+- Precision: 0.8519
+
+## 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: 0.0002
+- train_batch_size: 32
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
+| 0.6963        | 1.0   | 19   | 0.5246          | 0.76     | 0.7857 | 0.825  | 0.75      |
+| 0.6963        | 2.0   | 38   | 0.3911          | 0.8333   | 0.8322 | 0.775  | 0.8986    |
+| 0.6963        | 3.0   | 57   | 0.3062          | 0.8467   | 0.8571 | 0.8625 | 0.8519    |
+
+
+### Framework versions
+
+- Transformers 4.41.1
+- Pytorch 2.0.0+cu117
+- Datasets 2.19.1
+- Tokenizers 0.19.1

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