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---
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base_model: MBZUAI/swiftformer-xs
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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: swiftformer-xs-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: 0.8478260869565217
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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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# swiftformer-xs-ve-U13-b-80
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This model is a fine-tuned version of [MBZUAI/swiftformer-xs](https://huggingface.co./MBZUAI/swiftformer-xs) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6640
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- Accuracy: 0.8478
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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: 0.0003
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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: 100
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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 | 1.3858 | 0.2391 |
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| 1.3856 | 2.0 | 13 | 1.3828 | 0.2826 |
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| 1.3856 | 2.92 | 19 | 1.3769 | 0.1957 |
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| 1.3734 | 4.0 | 26 | 1.3624 | 0.1304 |
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| 1.2978 | 4.92 | 32 | 1.3553 | 0.1522 |
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| 1.2978 | 6.0 | 39 | 1.4121 | 0.0870 |
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| 1.1702 | 6.92 | 45 | 1.3720 | 0.2391 |
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| 1.0743 | 8.0 | 52 | 1.3162 | 0.3478 |
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| 1.0743 | 8.92 | 58 | 1.2252 | 0.3696 |
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| 0.9504 | 10.0 | 65 | 1.1689 | 0.4348 |
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| 0.8305 | 10.92 | 71 | 1.0516 | 0.5870 |
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| 0.8305 | 12.0 | 78 | 0.9548 | 0.6739 |
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| 0.7374 | 12.92 | 84 | 0.9138 | 0.7174 |
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| 0.6207 | 14.0 | 91 | 0.9353 | 0.6522 |
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| 0.6207 | 14.92 | 97 | 0.8640 | 0.6739 |
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| 0.5184 | 16.0 | 104 | 0.8122 | 0.7826 |
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| 0.4606 | 16.92 | 110 | 0.7136 | 0.8043 |
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| 0.4606 | 18.0 | 117 | 0.7955 | 0.7609 |
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| 0.4332 | 18.92 | 123 | 0.7790 | 0.6957 |
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| 0.3315 | 20.0 | 130 | 0.8117 | 0.7391 |
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| 0.3315 | 20.92 | 136 | 0.8068 | 0.7609 |
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| 0.3229 | 22.0 | 143 | 0.8786 | 0.7826 |
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| 0.3229 | 22.92 | 149 | 0.9030 | 0.7174 |
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| 0.3065 | 24.0 | 156 | 0.8253 | 0.6522 |
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| 0.2315 | 24.92 | 162 | 0.7398 | 0.8043 |
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| 0.2315 | 26.0 | 169 | 0.7939 | 0.7609 |
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| 0.222 | 26.92 | 175 | 0.6640 | 0.8478 |
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| 0.1756 | 28.0 | 182 | 0.8510 | 0.7391 |
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| 0.1756 | 28.92 | 188 | 0.9861 | 0.7174 |
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| 0.1702 | 30.0 | 195 | 1.1060 | 0.7609 |
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| 0.202 | 30.92 | 201 | 1.0929 | 0.7391 |
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| 0.202 | 32.0 | 208 | 0.8670 | 0.7826 |
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| 0.1665 | 32.92 | 214 | 0.8033 | 0.7609 |
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| 0.1695 | 34.0 | 221 | 0.7235 | 0.7826 |
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| 0.1695 | 34.92 | 227 | 0.8917 | 0.7609 |
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| 0.1807 | 36.0 | 234 | 0.9215 | 0.7391 |
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| 0.1289 | 36.92 | 240 | 0.8231 | 0.8043 |
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| 0.1289 | 38.0 | 247 | 0.9256 | 0.7826 |
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| 0.145 | 38.92 | 253 | 0.8866 | 0.7826 |
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| 0.1422 | 40.0 | 260 | 0.8511 | 0.8261 |
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| 0.1422 | 40.92 | 266 | 0.9956 | 0.7391 |
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| 0.1313 | 42.0 | 273 | 1.3005 | 0.7391 |
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| 0.1313 | 42.92 | 279 | 1.1532 | 0.6739 |
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| 0.1128 | 44.0 | 286 | 1.0891 | 0.7391 |
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| 0.1213 | 44.92 | 292 | 1.0765 | 0.7391 |
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| 0.1213 | 46.0 | 299 | 0.9142 | 0.7391 |
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| 0.1161 | 46.92 | 305 | 0.9100 | 0.7174 |
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| 0.1123 | 48.0 | 312 | 0.8907 | 0.7826 |
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| 0.1123 | 48.92 | 318 | 0.9462 | 0.7609 |
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| 0.1107 | 50.0 | 325 | 0.8592 | 0.7391 |
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| 0.0915 | 50.92 | 331 | 0.9894 | 0.7609 |
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| 0.0915 | 52.0 | 338 | 1.1094 | 0.7609 |
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| 0.0981 | 52.92 | 344 | 1.1956 | 0.7609 |
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| 0.0762 | 54.0 | 351 | 1.0079 | 0.7826 |
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| 0.0762 | 54.92 | 357 | 0.9899 | 0.7609 |
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| 0.1083 | 56.0 | 364 | 0.9164 | 0.7826 |
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| 0.1087 | 56.92 | 370 | 0.9263 | 0.7826 |
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| 0.1087 | 58.0 | 377 | 0.9160 | 0.7391 |
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| 0.0871 | 58.92 | 383 | 1.0179 | 0.7174 |
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| 0.0852 | 60.0 | 390 | 0.9246 | 0.7391 |
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| 0.0852 | 60.92 | 396 | 0.8929 | 0.8043 |
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| 0.0613 | 62.0 | 403 | 0.9989 | 0.7174 |
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| 0.0613 | 62.92 | 409 | 1.0367 | 0.7174 |
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| 0.0899 | 64.0 | 416 | 1.1213 | 0.6957 |
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| 0.0669 | 64.92 | 422 | 1.0093 | 0.7609 |
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| 0.0669 | 66.0 | 429 | 1.0129 | 0.7391 |
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| 0.0791 | 66.92 | 435 | 0.9979 | 0.7174 |
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| 0.0848 | 68.0 | 442 | 1.0137 | 0.7391 |
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| 0.0848 | 68.92 | 448 | 1.0761 | 0.6957 |
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| 0.0799 | 70.0 | 455 | 1.0152 | 0.6957 |
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| 0.0727 | 70.92 | 461 | 1.1302 | 0.6957 |
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| 0.0727 | 72.0 | 468 | 1.0468 | 0.7174 |
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| 0.0763 | 72.92 | 474 | 1.0759 | 0.6739 |
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| 0.06 | 74.0 | 481 | 1.0803 | 0.7174 |
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| 0.06 | 74.92 | 487 | 1.0484 | 0.6957 |
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| 0.0746 | 76.0 | 494 | 0.9999 | 0.7174 |
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| 0.0687 | 76.92 | 500 | 0.9937 | 0.7174 |
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| 0.0687 | 78.0 | 507 | 1.1189 | 0.6957 |
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| 0.0761 | 78.92 | 513 | 1.1013 | 0.6957 |
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| 0.0729 | 80.0 | 520 | 1.0294 | 0.6957 |
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| 0.0729 | 80.92 | 526 | 1.0860 | 0.7174 |
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| 0.0472 | 82.0 | 533 | 1.0327 | 0.7174 |
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| 0.0472 | 82.92 | 539 | 1.0225 | 0.7174 |
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| 0.0519 | 84.0 | 546 | 1.1345 | 0.6957 |
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| 0.0688 | 84.92 | 552 | 1.0923 | 0.6957 |
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| 0.0688 | 86.0 | 559 | 1.0876 | 0.7174 |
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| 0.0462 | 86.92 | 565 | 1.0740 | 0.6957 |
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| 0.0457 | 88.0 | 572 | 1.1074 | 0.6957 |
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| 0.0457 | 88.92 | 578 | 1.0777 | 0.6957 |
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| 0.0482 | 90.0 | 585 | 1.0495 | 0.7391 |
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| 0.0464 | 90.92 | 591 | 1.0395 | 0.7174 |
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| 0.0464 | 92.0 | 598 | 1.1446 | 0.7174 |
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| 0.0578 | 92.31 | 600 | 1.0596 | 0.6957 |
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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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