Model save

README.md

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+---
+license: apache-2.0
+base_model: microsoft/swin-tiny-patch4-window7-224
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: batch-size16_Celeb-DF_opencv-1FPS_unaugmentation
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: test
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.8481832909048831
+    - name: Precision
+      type: precision
+      value: 0.8503912115703786
+    - name: Recall
+      type: recall
+      value: 0.9930779880018459
+    - name: F1
+      type: f1
+      value: 0.9162125340599455
+---
+
+<!-- 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. -->
+
+# batch-size16_Celeb-DF_opencv-1FPS_unaugmentation
+
+This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3814
+- Accuracy: 0.8482
+- Precision: 0.8504
+- Recall: 0.9931
+- F1: 0.9162
+- Roc Auc: 0.7709
+
+## 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: 32
+- eval_batch_size: 32
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 128
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 1
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Roc Auc |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
+| 0.4011        | 0.9951 | 101  | 0.3814          | 0.8482   | 0.8504    | 0.9931 | 0.9162 | 0.7709  |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.1
+- Datasets 2.20.0
+- Tokenizers 0.19.1