Model save

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: microsoft/swinv2-tiny-patch4-window8-256
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: swinv2-tiny-patch4-window8-256-dmae-humeda-DAV39
+  results: []
+---
+
+<!-- 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-dmae-humeda-DAV39
+
+This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.6049
+- Accuracy: 0.7045
+
+## 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: 32
+- eval_batch_size: 32
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 128
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 30
+
+### Training results
+
+| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
+|:-------------:|:-------:|:----:|:---------------:|:--------:|
+| No log        | 1.0     | 4    | 1.3890          | 0.4318   |
+| No log        | 2.0     | 8    | 1.2016          | 0.5682   |
+| 4.424         | 3.0     | 12   | 1.0279          | 0.6364   |
+| 4.424         | 4.0     | 16   | 0.9227          | 0.6477   |
+| 4.424         | 5.0     | 20   | 0.9294          | 0.6818   |
+| 2.6292        | 6.0     | 24   | 0.9936          | 0.625    |
+| 2.6292        | 7.0     | 28   | 0.9816          | 0.6705   |
+| 2.6292        | 8.0     | 32   | 0.9091          | 0.75     |
+| 1.5117        | 9.0     | 36   | 0.8995          | 0.7159   |
+| 1.5117        | 10.0    | 40   | 1.0646          | 0.7045   |
+| 1.5117        | 11.0    | 44   | 1.1826          | 0.6705   |
+| 0.7053        | 12.0    | 48   | 1.2360          | 0.6932   |
+| 0.7053        | 13.0    | 52   | 1.4215          | 0.6818   |
+| 0.7053        | 14.0    | 56   | 1.3694          | 0.7045   |
+| 0.2599        | 15.0    | 60   | 1.2998          | 0.7159   |
+| 0.2599        | 16.0    | 64   | 1.3703          | 0.7159   |
+| 0.2599        | 17.0    | 68   | 1.4626          | 0.7159   |
+| 0.1282        | 18.0    | 72   | 1.5181          | 0.7159   |
+| 0.1282        | 19.0    | 76   | 1.5692          | 0.7045   |
+| 0.1282        | 20.0    | 80   | 1.5965          | 0.6932   |
+| 0.0816        | 21.0    | 84   | 1.6018          | 0.7045   |
+| 0.0816        | 22.0    | 88   | 1.6046          | 0.7045   |
+| 0.0816        | 22.6154 | 90   | 1.6049          | 0.7045   |
+
+
+### Framework versions
+
+- Transformers 4.47.1
+- Pytorch 2.5.1+cu124
+- Datasets 3.2.0
+- Tokenizers 0.21.0

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