update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: swinv2-small-patch4-window16-256-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: 0.9892592592592593
+    - name: F1
+      type: f1
+      value: 0.9892542163878574
+    - name: Precision
+      type: precision
+      value: 0.9892896521886161
+    - name: Recall
+      type: recall
+      value: 0.9892592592592593
+---
+
+<!-- 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. -->
+
+# swinv2-small-patch4-window16-256-finetuned-eurosat
+
+This model is a fine-tuned version of [microsoft/swinv2-small-patch4-window16-256](https://huggingface.co/microsoft/swinv2-small-patch4-window16-256) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0328
+- Accuracy: 0.9893
+- F1: 0.9893
+- Precision: 0.9893
+- Recall: 0.9893
+
+## 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: 0.0001
+- train_batch_size: 24
+- eval_batch_size: 24
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 96
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.2
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.2326        | 1.0   | 253  | 0.0870          | 0.9715   | 0.9716 | 0.9720    | 0.9715 |
+| 0.1955        | 2.0   | 506  | 0.0576          | 0.9789   | 0.9788 | 0.9794    | 0.9789 |
+| 0.1229        | 3.0   | 759  | 0.0450          | 0.9837   | 0.9837 | 0.9839    | 0.9837 |
+| 0.0797        | 4.0   | 1012 | 0.0332          | 0.9889   | 0.9889 | 0.9889    | 0.9889 |
+| 0.0826        | 5.0   | 1265 | 0.0328          | 0.9893   | 0.9893 | 0.9893    | 0.9893 |
+
+
+### Framework versions
+
+- Transformers 4.22.1
+- Pytorch 1.12.1+cu113
+- Datasets 2.5.1
+- Tokenizers 0.12.1