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: swin-tiny-patch4-window7-224-finetuned-FaceAIorNot-105330
+  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: 0.9935440994968195
+    - name: Precision
+      type: precision
+      value: 0.9925121677274429
+    - name: Recall
+      type: recall
+      value: 0.9947467166979362
+    - name: F1
+      type: f1
+      value: 0.9936281859070465
+---
+
+<!-- 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-tiny-patch4-window7-224-finetuned-FaceAIorNot-105330
+
+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: 0.0233
+- Accuracy: 0.9935
+- Precision: 0.9925
+- Recall: 0.9947
+- F1: 0.9936
+
+## 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: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 0.0862        | 1.0   | 740  | 0.0694          | 0.9740   | 0.9731    | 0.9756 | 0.9743 |
+| 0.0914        | 2.0   | 1481 | 0.0396          | 0.9862   | 0.9814    | 0.9916 | 0.9865 |
+| 0.0184        | 3.0   | 2222 | 0.0784          | 0.9777   | 0.9618    | 0.9955 | 0.9783 |
+| 0.0181        | 4.0   | 2963 | 0.0330          | 0.9907   | 0.9879    | 0.9938 | 0.9908 |
+| 0.03          | 4.99  | 3700 | 0.0233          | 0.9935   | 0.9925    | 0.9947 | 0.9936 |
+
+
+### Framework versions
+
+- Transformers 4.34.0
+- Pytorch 2.1.1+cu118
+- Datasets 2.14.5
+- Tokenizers 0.14.1