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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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- precision |
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- recall |
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model-index: |
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- name: swin-tiny-patch4-window7-224 |
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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: 0.7433333333333333 |
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- name: Precision |
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type: precision |
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value: 0.7306273291925466 |
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- name: Recall |
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type: recall |
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value: 0.7433333333333333 |
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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 |
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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: 0.5534 |
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- Accuracy: 0.7433 |
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- Precision: 0.7306 |
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- Recall: 0.7433 |
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- F1 Score: 0.7344 |
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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: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 256 |
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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: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:| |
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| No log | 1.0 | 4 | 0.7306 | 0.4 | 0.6521 | 0.4 | 0.3821 | |
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| No log | 2.0 | 8 | 0.5815 | 0.7333 | 0.8050 | 0.7333 | 0.6286 | |
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| No log | 3.0 | 12 | 0.5700 | 0.725 | 0.5256 | 0.725 | 0.6094 | |
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| No log | 4.0 | 16 | 0.5635 | 0.725 | 0.5256 | 0.725 | 0.6094 | |
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| No log | 5.0 | 20 | 0.5509 | 0.7292 | 0.8028 | 0.7292 | 0.6191 | |
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| No log | 6.0 | 24 | 0.5356 | 0.7417 | 0.7438 | 0.7417 | 0.6589 | |
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| No log | 7.0 | 28 | 0.5353 | 0.75 | 0.7360 | 0.75 | 0.6895 | |
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| No log | 8.0 | 32 | 0.5299 | 0.7375 | 0.7090 | 0.7375 | 0.6668 | |
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| No log | 9.0 | 36 | 0.5335 | 0.7667 | 0.7509 | 0.7667 | 0.7310 | |
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| No log | 10.0 | 40 | 0.5344 | 0.7417 | 0.7315 | 0.7417 | 0.6644 | |
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| No log | 11.0 | 44 | 0.5297 | 0.7458 | 0.7279 | 0.7458 | 0.6821 | |
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| No log | 12.0 | 48 | 0.5202 | 0.75 | 0.7360 | 0.75 | 0.6895 | |
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| 0.5942 | 13.0 | 52 | 0.5325 | 0.7542 | 0.7411 | 0.7542 | 0.7452 | |
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| 0.5942 | 14.0 | 56 | 0.5139 | 0.7583 | 0.7505 | 0.7583 | 0.7039 | |
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| 0.5942 | 15.0 | 60 | 0.5528 | 0.7417 | 0.7347 | 0.7417 | 0.7377 | |
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| 0.5942 | 16.0 | 64 | 0.5070 | 0.7625 | 0.7437 | 0.7625 | 0.7277 | |
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| 0.5942 | 17.0 | 68 | 0.5193 | 0.775 | 0.7594 | 0.775 | 0.7592 | |
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| 0.5942 | 18.0 | 72 | 0.5090 | 0.7583 | 0.7448 | 0.7583 | 0.7487 | |
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| 0.5942 | 19.0 | 76 | 0.5189 | 0.7792 | 0.7847 | 0.7792 | 0.7816 | |
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| 0.5942 | 20.0 | 80 | 0.5214 | 0.775 | 0.7795 | 0.775 | 0.7770 | |
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| 0.5942 | 21.0 | 84 | 0.5188 | 0.775 | 0.7710 | 0.775 | 0.7728 | |
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| 0.5942 | 22.0 | 88 | 0.5029 | 0.7667 | 0.7526 | 0.7667 | 0.7557 | |
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| 0.5942 | 23.0 | 92 | 0.5061 | 0.7833 | 0.7734 | 0.7833 | 0.7761 | |
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| 0.5942 | 24.0 | 96 | 0.5350 | 0.7667 | 0.7713 | 0.7667 | 0.7687 | |
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| 0.4829 | 25.0 | 100 | 0.5149 | 0.7542 | 0.7330 | 0.7542 | 0.7337 | |
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| 0.4829 | 26.0 | 104 | 0.5283 | 0.7583 | 0.7737 | 0.7583 | 0.7641 | |
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| 0.4829 | 27.0 | 108 | 0.5109 | 0.7792 | 0.7647 | 0.7792 | 0.7646 | |
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| 0.4829 | 28.0 | 112 | 0.5258 | 0.775 | 0.7729 | 0.775 | 0.7739 | |
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| 0.4829 | 29.0 | 116 | 0.5207 | 0.7625 | 0.745 | 0.7625 | 0.7468 | |
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| 0.4829 | 30.0 | 120 | 0.5306 | 0.75 | 0.7357 | 0.75 | 0.7400 | |
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| 0.4829 | 31.0 | 124 | 0.5455 | 0.75 | 0.7375 | 0.75 | 0.7417 | |
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| 0.4829 | 32.0 | 128 | 0.5653 | 0.7458 | 0.7380 | 0.7458 | 0.7412 | |
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| 0.4829 | 33.0 | 132 | 0.5565 | 0.7417 | 0.7212 | 0.7417 | 0.7256 | |
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| 0.4829 | 34.0 | 136 | 0.5468 | 0.7708 | 0.7658 | 0.7708 | 0.7679 | |
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| 0.4829 | 35.0 | 140 | 0.5268 | 0.7833 | 0.7723 | 0.7833 | 0.7747 | |
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| 0.4829 | 36.0 | 144 | 0.5260 | 0.775 | 0.7710 | 0.775 | 0.7728 | |
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| 0.4829 | 37.0 | 148 | 0.5281 | 0.775 | 0.7659 | 0.775 | 0.7689 | |
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| 0.3846 | 38.0 | 152 | 0.5385 | 0.7708 | 0.7742 | 0.7708 | 0.7724 | |
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| 0.3846 | 39.0 | 156 | 0.5253 | 0.7708 | 0.7623 | 0.7708 | 0.7653 | |
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| 0.3846 | 40.0 | 160 | 0.5319 | 0.7708 | 0.7719 | 0.7708 | 0.7714 | |
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| 0.3846 | 41.0 | 164 | 0.5311 | 0.775 | 0.7631 | 0.775 | 0.7660 | |
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| 0.3846 | 42.0 | 168 | 0.5325 | 0.7792 | 0.7683 | 0.7792 | 0.7711 | |
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| 0.3846 | 43.0 | 172 | 0.5254 | 0.7667 | 0.7606 | 0.7667 | 0.7631 | |
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| 0.3846 | 44.0 | 176 | 0.5232 | 0.7708 | 0.7623 | 0.7708 | 0.7653 | |
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| 0.3846 | 45.0 | 180 | 0.5291 | 0.7708 | 0.7640 | 0.7708 | 0.7667 | |
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| 0.3846 | 46.0 | 184 | 0.5356 | 0.7708 | 0.7607 | 0.7708 | 0.7639 | |
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| 0.3846 | 47.0 | 188 | 0.5400 | 0.7708 | 0.7607 | 0.7708 | 0.7639 | |
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| 0.3846 | 48.0 | 192 | 0.5409 | 0.7667 | 0.7540 | 0.7667 | 0.7573 | |
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| 0.3846 | 49.0 | 196 | 0.5403 | 0.7667 | 0.7540 | 0.7667 | 0.7573 | |
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| 0.3353 | 50.0 | 200 | 0.5397 | 0.7708 | 0.7592 | 0.7708 | 0.7624 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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