Model save

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: microsoft/swin-tiny-patch4-window7-224
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: swin-tiny-patch4-window7-224-image-classifier
+  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: 0.748792270531401
+    - name: F1
+      type: f1
+      value: 0.655421686746988
+    - name: Precision
+      type: precision
+      value: 0.6267281105990783
+    - name: Recall
+      type: recall
+      value: 0.6868686868686869
+---
+
+<!-- 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-image-classifier
+
+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: 0.4362
+- Accuracy: 0.7488
+- F1: 0.6554
+- Precision: 0.6267
+- Recall: 0.6869
+
+## 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: 1e-05
+- train_batch_size: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.5982        | 1.0   | 143  | 0.5693          | 0.6711   | 0.4144 | 0.5441    | 0.3346 |
+| 0.4391        | 2.0   | 286  | 0.4924          | 0.7295   | 0.4849 | 0.7178    | 0.3662 |
+| 0.3658        | 3.0   | 429  | 0.4332          | 0.7501   | 0.6459 | 0.6368    | 0.6553 |
+| 0.3404        | 4.0   | 572  | 0.4202          | 0.7694   | 0.6525 | 0.6857    | 0.6225 |
+| 0.3188        | 5.0   | 715  | 0.4362          | 0.7488   | 0.6554 | 0.6267    | 0.6869 |
+
+
+### Framework versions
+
+- Transformers 4.46.0
+- Pytorch 2.5.0+cu121
+- Datasets 3.0.2
+- Tokenizers 0.20.1