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
+- f1
+model-index:
+- name: swin-tiny-patch4-window7-224-finetuned-eurosat
+  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: 0.9803703703703703
+    - name: F1
+      type: f1
+      value: 0.9801864359704018
+---
+
+<!-- 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-eurosat
+
+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: 0.0601
+- Accuracy: 0.9804
+- F1: 0.9802
+
+## 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: 64
+- eval_batch_size: 64
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 256
+- 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 | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
+| 0.3306        | 1.0   | 95   | 0.1438          | 0.9563   | 0.9557 |
+| 0.1922        | 2.0   | 190  | 0.0962          | 0.9704   | 0.9700 |
+| 0.1749        | 3.0   | 285  | 0.0770          | 0.9752   | 0.9746 |
+| 0.124         | 4.0   | 380  | 0.0646          | 0.9785   | 0.9782 |
+| 0.1147        | 5.0   | 475  | 0.0601          | 0.9804   | 0.9802 |
+
+
+### Framework versions
+
+- Transformers 4.41.1
+- Pytorch 2.0.0+cu117
+- Datasets 2.19.1
+- Tokenizers 0.19.1