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---
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license: apache-2.0
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base_model: microsoft/swin-tiny-patch4-window7-224
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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: swin-tiny-patch4-window7-224-finetuned-wsdmhar
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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: train
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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: 0.9297520661157025
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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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# swin-tiny-patch4-window7-224-finetuned-wsdmhar
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This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co./microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1990
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- Accuracy: 0.9298
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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: 5e-05
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 128
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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: 10
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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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| 0.7388 | 1.0 | 53 | 0.6308 | 0.7118 |
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| 0.5099 | 2.0 | 106 | 0.3669 | 0.8485 |
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| 0.4319 | 3.0 | 159 | 0.3324 | 0.8685 |
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| 0.4002 | 4.0 | 212 | 0.2758 | 0.9029 |
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| 0.3589 | 5.0 | 265 | 0.2503 | 0.9132 |
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| 0.3096 | 6.0 | 318 | 0.2419 | 0.9136 |
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| 0.2708 | 7.0 | 371 | 0.2277 | 0.9232 |
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| 0.261 | 8.0 | 424 | 0.2168 | 0.9253 |
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| 0.2526 | 9.0 | 477 | 0.2099 | 0.9246 |
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| 0.2767 | 10.0 | 530 | 0.1990 | 0.9298 |
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### Framework versions
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- Transformers 4.43.2
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- Pytorch 2.3.1+cu118
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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