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
+- precision
+- recall
+model-index:
+- name: VIT-MFCC-Synthetic-Voice-Detection
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: validation
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9804379327000483
+    - name: F1
+      type: f1
+      value: 0.9892177308426143
+    - name: Precision
+      type: precision
+      value: 0.9787514268153481
+    - name: Recall
+      type: recall
+      value: 0.9999102978112666
+---
+
+<!-- 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-MFCC-Synthetic-Voice-Detection
+
+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: 0.1213
+- Accuracy: 0.9804
+- F1: 0.9892
+- Precision: 0.9788
+- Recall: 0.9999
+
+## 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: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3.0
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.0283        | 1.0   | 3173 | 0.0958          | 0.9797   | 0.9888 | 0.9782    | 0.9996 |
+| 0.0227        | 2.0   | 6346 | 0.0597          | 0.9874   | 0.9930 | 0.9890    | 0.9971 |
+| 0.0036        | 3.0   | 9519 | 0.1213          | 0.9804   | 0.9892 | 0.9788    | 0.9999 |
+
+
+### Framework versions
+
+- Transformers 4.36.2
+- Pytorch 2.1.2+cu121
+- Datasets 2.15.0
+- Tokenizers 0.15.0

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