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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-ve-U11-b-40
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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: 0.8260869565217391
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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-ve-U11-b-40
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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: 0.6121
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- Accuracy: 0.8261
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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 | 0.92 | 6 | 1.5799 | 0.4783 |
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| 2.1773 | 2.0 | 13 | 1.5648 | 0.3478 |
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| 2.1773 | 2.92 | 19 | 1.5182 | 0.3261 |
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| 2.1773 | 4.0 | 26 | 1.4232 | 0.3261 |
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| 1.8993 | 4.92 | 32 | 1.3505 | 0.3913 |
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| 1.8993 | 6.0 | 39 | 1.2747 | 0.3696 |
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| 1.5045 | 6.92 | 45 | 1.2452 | 0.3696 |
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| 1.2431 | 8.0 | 52 | 1.1982 | 0.2826 |
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| 1.2431 | 8.92 | 58 | 1.2112 | 0.3043 |
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| 1.1225 | 10.0 | 65 | 1.0160 | 0.5 |
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| 0.9942 | 10.92 | 71 | 1.0138 | 0.4783 |
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| 0.9942 | 12.0 | 78 | 0.9094 | 0.5652 |
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| 0.9212 | 12.92 | 84 | 0.8860 | 0.5217 |
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| 0.816 | 14.0 | 91 | 0.7693 | 0.6739 |
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| 0.816 | 14.92 | 97 | 0.8290 | 0.6304 |
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| 0.741 | 16.0 | 104 | 0.7810 | 0.6739 |
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| 0.631 | 16.92 | 110 | 0.6342 | 0.7826 |
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| 0.631 | 18.0 | 117 | 0.7677 | 0.6957 |
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| 0.6402 | 18.92 | 123 | 0.6283 | 0.7391 |
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| 0.5477 | 20.0 | 130 | 0.6687 | 0.7174 |
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| 0.5477 | 20.92 | 136 | 0.6369 | 0.7826 |
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| 0.5023 | 22.0 | 143 | 0.6334 | 0.7609 |
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| 0.5023 | 22.92 | 149 | 0.6355 | 0.8043 |
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| 0.4802 | 24.0 | 156 | 0.5976 | 0.8043 |
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| 0.4336 | 24.92 | 162 | 0.6112 | 0.7609 |
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| 0.4336 | 26.0 | 169 | 0.6148 | 0.8043 |
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| 0.4203 | 26.92 | 175 | 0.6380 | 0.7391 |
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| 0.429 | 28.0 | 182 | 0.6032 | 0.8043 |
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| 0.429 | 28.92 | 188 | 0.6348 | 0.7391 |
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| 0.4013 | 30.0 | 195 | 0.6121 | 0.8261 |
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| 0.3747 | 30.92 | 201 | 0.6521 | 0.7391 |
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| 0.3747 | 32.0 | 208 | 0.6424 | 0.7609 |
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| 0.3668 | 32.92 | 214 | 0.6149 | 0.8261 |
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| 0.3287 | 34.0 | 221 | 0.6426 | 0.7826 |
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| 0.3287 | 34.92 | 227 | 0.6379 | 0.8043 |
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| 0.372 | 36.0 | 234 | 0.6435 | 0.8043 |
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| 0.3236 | 36.92 | 240 | 0.6450 | 0.8043 |
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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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