Model save

README.md

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+---
+license: apache-2.0
+base_model: microsoft/swinv2-tiny-patch4-window8-256
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: swinv2-finetuned-ve-Ub200
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: validation
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.47058823529411764
+---
+
+<!-- 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-finetuned-ve-Ub200
+
+This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.1771
+- Accuracy: 0.4706
+
+## 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: 16
+- eval_batch_size: 16
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 20
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| No log        | 0.92  | 6    | 7.9891          | 0.0980   |
+| No log        | 2.0   | 13   | 7.4848          | 0.0980   |
+| No log        | 2.92  | 19   | 6.2378          | 0.0980   |
+| No log        | 4.0   | 26   | 4.8900          | 0.0980   |
+| No log        | 4.92  | 32   | 3.8155          | 0.0980   |
+| No log        | 6.0   | 39   | 2.7342          | 0.0980   |
+| No log        | 6.92  | 45   | 2.0612          | 0.0980   |
+| No log        | 8.0   | 52   | 1.5977          | 0.4706   |
+| No log        | 8.92  | 58   | 1.3671          | 0.4706   |
+| No log        | 10.0  | 65   | 1.2122          | 0.4706   |
+| No log        | 10.92 | 71   | 1.1823          | 0.4706   |
+| No log        | 12.0  | 78   | 1.1835          | 0.4706   |
+| No log        | 12.92 | 84   | 1.1838          | 0.4706   |
+| No log        | 14.0  | 91   | 1.1778          | 0.4706   |
+| No log        | 14.92 | 97   | 1.1769          | 0.4706   |
+| 3.2267        | 16.0  | 104  | 1.1762          | 0.4706   |
+| 3.2267        | 16.92 | 110  | 1.1758          | 0.4706   |
+| 3.2267        | 18.0  | 117  | 1.1770          | 0.4706   |
+| 3.2267        | 18.46 | 120  | 1.1771          | 0.4706   |
+
+
+### Framework versions
+
+- Transformers 4.36.2
+- Pytorch 2.1.2+cu118
+- Datasets 2.16.1
+- Tokenizers 0.15.0

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