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---
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license: apache-2.0
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base_model: microsoft/swinv2-tiny-patch4-window8-256
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tags:
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- generated_from_trainer
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datasets:
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- imagefolder
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metrics:
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- accuracy
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model-index:
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- name: swinv2-tiny-patch4-window8-256-DMAE-8e-6
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results:
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- task:
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name: Image Classification
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type: image-classification
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dataset:
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name: imagefolder
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type: imagefolder
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config: default
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split: test
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args: default
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.10869565217391304
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# swinv2-tiny-patch4-window8-256-DMAE-8e-6
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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.
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It achieves the following results on the evaluation set:
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- Loss: 6.3664
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- Accuracy: 0.1087
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 8e-06
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 64
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 40
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| No log | 0.86 | 3 | 7.9427 | 0.1087 |
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| No log | 2.0 | 7 | 7.9381 | 0.1087 |
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| 7.9636 | 2.86 | 10 | 7.9301 | 0.1087 |
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| 7.9636 | 4.0 | 14 | 7.9088 | 0.1087 |
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| 7.9636 | 4.86 | 17 | 7.8857 | 0.1087 |
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| 7.8732 | 6.0 | 21 | 7.8450 | 0.1087 |
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| 7.8732 | 6.86 | 24 | 7.8049 | 0.1087 |
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| 7.8732 | 8.0 | 28 | 7.7376 | 0.1087 |
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| 7.6568 | 8.86 | 31 | 7.6783 | 0.1087 |
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| 7.6568 | 10.0 | 35 | 7.5943 | 0.1087 |
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| 7.6568 | 10.86 | 38 | 7.5288 | 0.1087 |
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| 7.7458 | 12.0 | 42 | 7.4353 | 0.1087 |
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| 7.7458 | 12.86 | 45 | 7.3610 | 0.1087 |
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| 7.7458 | 14.0 | 49 | 7.2614 | 0.1087 |
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| 7.3025 | 14.86 | 52 | 7.1894 | 0.1087 |
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| 7.3025 | 16.0 | 56 | 7.0993 | 0.1087 |
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| 7.3025 | 16.86 | 59 | 7.0348 | 0.1087 |
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| 7.0862 | 18.0 | 63 | 6.9525 | 0.1087 |
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| 7.0862 | 18.86 | 66 | 6.8945 | 0.1087 |
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| 6.9553 | 20.0 | 70 | 6.8253 | 0.1087 |
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| 6.9553 | 20.86 | 73 | 6.7795 | 0.1087 |
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| 6.9553 | 22.0 | 77 | 6.7202 | 0.1087 |
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| 6.8024 | 22.86 | 80 | 6.6757 | 0.1087 |
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| 6.8024 | 24.0 | 84 | 6.6210 | 0.1087 |
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| 6.8024 | 24.86 | 87 | 6.5785 | 0.1087 |
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| 6.6652 | 26.0 | 91 | 6.5275 | 0.1087 |
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| 6.6652 | 26.86 | 94 | 6.4949 | 0.1087 |
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| 6.6652 | 28.0 | 98 | 6.4589 | 0.1087 |
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| 6.467 | 28.86 | 101 | 6.4354 | 0.1087 |
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| 6.467 | 30.0 | 105 | 6.4094 | 0.1087 |
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| 6.467 | 30.86 | 108 | 6.3946 | 0.1087 |
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| 6.4984 | 32.0 | 112 | 6.3796 | 0.1087 |
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| 6.4984 | 32.86 | 115 | 6.3719 | 0.1087 |
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| 6.4984 | 34.0 | 119 | 6.3668 | 0.1087 |
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| 6.4603 | 34.29 | 120 | 6.3664 | 0.1087 |
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### Framework versions
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- Transformers 4.36.2
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- Pytorch 2.1.2+cu118
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- Datasets 2.16.1
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- Tokenizers 0.15.0
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