Training in progress, epoch 0

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-tiny-patch4-window8-256-ve-Ub
+  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: 0.09803921568627451
+---
+
+<!-- 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-tiny-patch4-window8-256-ve-Ub
+
+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: 53618207116050790111890112512.0000
+- Accuracy: 0.0980
+
+## 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: 40
+
+### Training results
+
+| Training Loss                      | Epoch | Step | Validation Loss                    | Accuracy |
+|:----------------------------------:|:-----:|:----:|:----------------------------------:|:--------:|
+| No log                             | 0.57  | 1    | 53618207116050790111890112512.0000 | 0.0980   |
+| No log                             | 1.71  | 3    | 53618207116050790111890112512.0000 | 0.0980   |
+| No log                             | 2.86  | 5    | 53618207116050790111890112512.0000 | 0.0980   |
+| No log                             | 4.0   | 7    | 53618207116050790111890112512.0000 | 0.0980   |
+| No log                             | 4.57  | 8    | 53618207116050790111890112512.0000 | 0.0980   |
+| 52775460204830951003775827968.0000 | 5.71  | 10   | 53618207116050790111890112512.0000 | 0.0980   |
+| 52775460204830951003775827968.0000 | 6.86  | 12   | 53618207116050790111890112512.0000 | 0.0980   |
+| 52775460204830951003775827968.0000 | 8.0   | 14   | 53618207116050790111890112512.0000 | 0.0980   |
+| 52775460204830951003775827968.0000 | 8.57  | 15   | 53618207116050790111890112512.0000 | 0.0980   |
+| 52775460204830951003775827968.0000 | 9.71  | 17   | 53618207116050790111890112512.0000 | 0.0980   |
+| 52775460204830951003775827968.0000 | 10.86 | 19   | 53618207116050790111890112512.0000 | 0.0980   |
+| 52792343609480503040906625024.0000 | 12.0  | 21   | 53618207116050790111890112512.0000 | 0.0980   |
+| 52792343609480503040906625024.0000 | 12.57 | 22   | 53618207116050790111890112512.0000 | 0.0980   |
+| 52792343609480503040906625024.0000 | 13.71 | 24   | 53618207116050790111890112512.0000 | 0.0980   |
+| 52792343609480503040906625024.0000 | 14.86 | 26   | 53618207116050790111890112512.0000 | 0.0980   |
+| 52792343609480503040906625024.0000 | 16.0  | 28   | 53618207116050790111890112512.0000 | 0.0980   |
+| 52792343609480503040906625024.0000 | 16.57 | 29   | 53618207116050790111890112512.0000 | 0.0980   |
+| 52872559615505121741583155200.0000 | 17.71 | 31   | 53618207116050790111890112512.0000 | 0.0980   |
+| 52872559615505121741583155200.0000 | 18.86 | 33   | 53618207116050790111890112512.0000 | 0.0980   |
+| 52872559615505121741583155200.0000 | 20.0  | 35   | 53618207116050790111890112512.0000 | 0.0980   |
+| 52872559615505121741583155200.0000 | 20.57 | 36   | 53618207116050790111890112512.0000 | 0.0980   |
+| 52872559615505121741583155200.0000 | 21.71 | 38   | 53618207116050790111890112512.0000 | 0.0980   |
+| 52783892462422761283050799104.0000 | 22.86 | 40   | 53618207116050790111890112512.0000 | 0.0980   |
+
+
+### Framework versions
+
+- Transformers 4.36.2
+- Pytorch 2.1.2+cu118
+- Datasets 2.16.1
+- Tokenizers 0.15.0

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