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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-U13-b-80
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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: 0.8043478260869565
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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-U13-b-80
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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: 0.9190
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- Accuracy: 0.8043
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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: 80
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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 | 1.3859 | 0.1304 |
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| 1.3859 | 2.0 | 13 | 1.3828 | 0.2826 |
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| 1.3859 | 2.92 | 19 | 1.3769 | 0.3261 |
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| 1.379 | 4.0 | 26 | 1.3566 | 0.2826 |
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| 1.3356 | 4.92 | 32 | 1.3162 | 0.2391 |
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| 1.3356 | 6.0 | 39 | 1.2093 | 0.3478 |
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| 1.2023 | 6.92 | 45 | 1.1349 | 0.4565 |
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| 1.0274 | 8.0 | 52 | 1.0414 | 0.4783 |
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| 1.0274 | 8.92 | 58 | 0.9788 | 0.5217 |
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| 0.9125 | 10.0 | 65 | 1.0071 | 0.4348 |
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| 0.7688 | 10.92 | 71 | 1.0416 | 0.5217 |
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| 0.7688 | 12.0 | 78 | 1.0480 | 0.4130 |
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| 0.6891 | 12.92 | 84 | 0.9351 | 0.5870 |
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| 0.5795 | 14.0 | 91 | 1.0683 | 0.6304 |
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| 0.5795 | 14.92 | 97 | 1.0698 | 0.6087 |
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| 0.5337 | 16.0 | 104 | 0.9603 | 0.6304 |
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| 0.4337 | 16.92 | 110 | 0.7188 | 0.6957 |
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| 0.4337 | 18.0 | 117 | 0.7620 | 0.6739 |
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| 0.4258 | 18.92 | 123 | 0.9433 | 0.6739 |
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| 0.4045 | 20.0 | 130 | 1.0823 | 0.6522 |
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| 0.4045 | 20.92 | 136 | 0.7059 | 0.7174 |
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| 0.4135 | 22.0 | 143 | 0.7467 | 0.7391 |
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| 0.4135 | 22.92 | 149 | 0.7637 | 0.7391 |
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| 0.3525 | 24.0 | 156 | 0.8157 | 0.7391 |
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| 0.263 | 24.92 | 162 | 0.9995 | 0.7174 |
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| 0.263 | 26.0 | 169 | 0.8719 | 0.7609 |
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| 0.272 | 26.92 | 175 | 0.9939 | 0.6957 |
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| 0.262 | 28.0 | 182 | 0.8639 | 0.7174 |
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| 0.262 | 28.92 | 188 | 1.0737 | 0.6522 |
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| 0.2282 | 30.0 | 195 | 0.8416 | 0.7174 |
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| 0.2098 | 30.92 | 201 | 0.9744 | 0.6739 |
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| 0.2098 | 32.0 | 208 | 1.0593 | 0.6087 |
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| 0.2141 | 32.92 | 214 | 1.0997 | 0.7174 |
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| 0.1759 | 34.0 | 221 | 0.9735 | 0.5870 |
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| 0.1759 | 34.92 | 227 | 1.0789 | 0.6957 |
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| 0.2042 | 36.0 | 234 | 1.0664 | 0.6957 |
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| 0.1591 | 36.92 | 240 | 0.9417 | 0.7609 |
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| 0.1591 | 38.0 | 247 | 1.1042 | 0.6739 |
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| 0.1579 | 38.92 | 253 | 0.9732 | 0.7609 |
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| 0.1626 | 40.0 | 260 | 0.9960 | 0.6957 |
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| 0.1626 | 40.92 | 266 | 0.9763 | 0.7391 |
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| 0.1458 | 42.0 | 273 | 0.9790 | 0.7391 |
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| 0.1458 | 42.92 | 279 | 1.0952 | 0.7174 |
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| 0.1317 | 44.0 | 286 | 0.9190 | 0.8043 |
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| 0.1255 | 44.92 | 292 | 0.9420 | 0.7391 |
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| 0.1255 | 46.0 | 299 | 0.9085 | 0.7391 |
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| 0.1352 | 46.92 | 305 | 0.9184 | 0.7174 |
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| 0.1311 | 48.0 | 312 | 1.0567 | 0.7609 |
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| 0.1311 | 48.92 | 318 | 1.1507 | 0.7174 |
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| 0.1501 | 50.0 | 325 | 1.2068 | 0.7174 |
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| 0.1088 | 50.92 | 331 | 1.4607 | 0.6957 |
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| 0.1088 | 52.0 | 338 | 1.1036 | 0.6739 |
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| 0.1152 | 52.92 | 344 | 1.1081 | 0.6957 |
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| 0.1141 | 54.0 | 351 | 1.1006 | 0.6957 |
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| 0.1141 | 54.92 | 357 | 1.1470 | 0.7174 |
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| 0.1307 | 56.0 | 364 | 1.0715 | 0.7609 |
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| 0.1273 | 56.92 | 370 | 1.1021 | 0.7174 |
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| 0.1273 | 58.0 | 377 | 1.1176 | 0.6957 |
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| 0.1066 | 58.92 | 383 | 1.0948 | 0.7174 |
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| 0.1046 | 60.0 | 390 | 1.0563 | 0.7391 |
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| 0.1046 | 60.92 | 396 | 1.1155 | 0.6957 |
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| 0.1129 | 62.0 | 403 | 1.0922 | 0.6957 |
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| 0.1129 | 62.92 | 409 | 1.0364 | 0.6957 |
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| 0.1031 | 64.0 | 416 | 1.0675 | 0.7174 |
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| 0.0808 | 64.92 | 422 | 1.1133 | 0.6957 |
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| 0.0808 | 66.0 | 429 | 1.2029 | 0.7174 |
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| 0.0783 | 66.92 | 435 | 1.1453 | 0.7174 |
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| 0.09 | 68.0 | 442 | 1.0925 | 0.6957 |
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| 0.09 | 68.92 | 448 | 1.0999 | 0.7174 |
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| 0.0796 | 70.0 | 455 | 1.0971 | 0.7391 |
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| 0.0828 | 70.92 | 461 | 1.0923 | 0.7391 |
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| 0.0828 | 72.0 | 468 | 1.1061 | 0.7391 |
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| 0.0923 | 72.92 | 474 | 1.1173 | 0.7391 |
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| 0.092 | 73.85 | 480 | 1.1208 | 0.7391 |
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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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