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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-ve-Ub
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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: validation
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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: 0.09803921568627451
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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-ve-Ub
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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: 8.0201
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- Accuracy: 0.0980
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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: 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 | 0.57 | 1 | 8.0201 | 0.0980 |
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| No log | 1.71 | 3 | 8.0044 | 0.0980 |
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| No log | 2.86 | 5 | 7.9306 | 0.0980 |
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| No log | 4.0 | 7 | 7.7713 | 0.0980 |
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| No log | 4.57 | 8 | 7.6511 | 0.0980 |
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| 7.7785 | 5.71 | 10 | 7.3653 | 0.0980 |
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| 7.7785 | 6.86 | 12 | 7.0246 | 0.0980 |
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| 7.7785 | 8.0 | 14 | 6.6413 | 0.0980 |
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| 7.7785 | 8.57 | 15 | 6.4670 | 0.0980 |
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| 7.7785 | 9.71 | 17 | 6.1321 | 0.0980 |
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| 7.7785 | 10.86 | 19 | 5.8360 | 0.0980 |
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| 6.5357 | 12.0 | 21 | 5.5743 | 0.0980 |
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| 6.5357 | 12.57 | 22 | 5.4552 | 0.0980 |
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| 6.5357 | 13.71 | 24 | 5.2367 | 0.0980 |
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| 6.5357 | 14.86 | 26 | 5.0418 | 0.0980 |
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| 6.5357 | 16.0 | 28 | 4.8706 | 0.0980 |
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| 6.5357 | 16.57 | 29 | 4.7939 | 0.0980 |
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| 5.2494 | 17.71 | 31 | 4.6596 | 0.0980 |
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| 5.2494 | 18.86 | 33 | 4.5508 | 0.0980 |
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| 5.2494 | 20.0 | 35 | 4.4676 | 0.0980 |
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| 5.2494 | 20.57 | 36 | 4.4356 | 0.0980 |
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| 5.2494 | 21.71 | 38 | 4.3906 | 0.0980 |
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| 4.5614 | 22.86 | 40 | 4.3714 | 0.0980 |
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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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