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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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metrics: |
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- accuracy |
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model-index: |
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- name: swim2-base-model |
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results: [] |
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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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# swim2-base-model |
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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 an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9308 |
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- Accuracy: 0.5227 |
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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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- 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: 3 |
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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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| 0.5804 | 0.05 | 100 | 0.5395 | 0.7388 | |
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| 0.2244 | 0.09 | 200 | 0.3057 | 0.8787 | |
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| 0.134 | 0.14 | 300 | 0.2218 | 0.9129 | |
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| 0.1786 | 0.18 | 400 | 0.1567 | 0.9373 | |
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| 0.0924 | 0.23 | 500 | 0.1360 | 0.9464 | |
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| 0.1217 | 0.27 | 600 | 0.1732 | 0.9349 | |
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| 0.144 | 0.32 | 700 | 0.1233 | 0.9538 | |
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| 0.0917 | 0.37 | 800 | 0.1655 | 0.9379 | |
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| 0.1005 | 0.41 | 900 | 0.1047 | 0.9632 | |
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| 0.1391 | 0.46 | 1000 | 0.1281 | 0.9554 | |
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| 0.0488 | 0.5 | 1100 | 0.0965 | 0.9688 | |
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| 0.0587 | 0.55 | 1200 | 0.1926 | 0.9460 | |
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| 0.0827 | 0.59 | 1300 | 0.0982 | 0.9630 | |
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| 0.035 | 0.64 | 1400 | 0.1011 | 0.9676 | |
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| 0.0529 | 0.69 | 1500 | 0.0984 | 0.9646 | |
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| 0.0653 | 0.73 | 1600 | 0.0877 | 0.9666 | |
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| 0.0749 | 0.78 | 1700 | 0.1208 | 0.9604 | |
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| 0.0686 | 0.82 | 1800 | 0.0742 | 0.9719 | |
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| 0.039 | 0.87 | 1900 | 0.0829 | 0.9717 | |
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| 0.0607 | 0.91 | 2000 | 0.0767 | 0.9746 | |
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| 0.0478 | 0.96 | 2100 | 0.0789 | 0.9725 | |
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| 0.0408 | 1.01 | 2200 | 0.0750 | 0.9757 | |
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| 0.0228 | 1.05 | 2300 | 0.0707 | 0.9773 | |
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| 0.0431 | 1.1 | 2400 | 0.0690 | 0.9787 | |
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| 0.0675 | 1.14 | 2500 | 0.0712 | 0.9773 | |
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| 0.0624 | 1.19 | 2600 | 0.1109 | 0.9640 | |
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| 0.0843 | 1.23 | 2700 | 0.1077 | 0.9692 | |
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| 0.0328 | 1.28 | 2800 | 0.0663 | 0.9795 | |
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| 0.0724 | 1.33 | 2900 | 0.0811 | 0.9766 | |
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| 0.0385 | 1.37 | 3000 | 0.0820 | 0.9732 | |
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| 0.0315 | 1.42 | 3100 | 0.0711 | 0.9788 | |
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| 0.0367 | 1.46 | 3200 | 0.0806 | 0.9765 | |
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| 0.0382 | 1.51 | 3300 | 0.1444 | 0.9612 | |
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| 0.024 | 1.55 | 3400 | 0.1038 | 0.9738 | |
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| 0.0331 | 1.6 | 3500 | 0.1181 | 0.9660 | |
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| 0.0419 | 1.65 | 3600 | 0.0687 | 0.9790 | |
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| 0.0352 | 1.69 | 3700 | 0.0687 | 0.9789 | |
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| 0.0588 | 1.74 | 3800 | 0.0620 | 0.9804 | |
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| 0.0313 | 1.78 | 3900 | 0.0975 | 0.9722 | |
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| 0.0421 | 1.83 | 4000 | 0.0588 | 0.9803 | |
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| 0.0182 | 1.87 | 4100 | 0.0601 | 0.9819 | |
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| 0.0323 | 1.92 | 4200 | 0.0593 | 0.9819 | |
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| 0.0565 | 1.97 | 4300 | 0.0537 | 0.9820 | |
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| 0.0266 | 2.01 | 4400 | 0.0693 | 0.9804 | |
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| 0.0374 | 2.06 | 4500 | 0.0610 | 0.9819 | |
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| 0.0246 | 2.1 | 4600 | 0.0580 | 0.9822 | |
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| 0.0316 | 2.15 | 4700 | 0.0674 | 0.9804 | |
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| 0.0415 | 2.19 | 4800 | 0.0569 | 0.9826 | |
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| 0.0361 | 2.24 | 4900 | 0.0550 | 0.9840 | |
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| 0.0298 | 2.29 | 5000 | 0.0575 | 0.9830 | |
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| 0.0275 | 2.33 | 5100 | 0.0600 | 0.9836 | |
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| 0.0194 | 2.38 | 5200 | 0.0678 | 0.9825 | |
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| 0.0279 | 2.42 | 5300 | 0.0608 | 0.9838 | |
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| 0.0585 | 2.47 | 5400 | 0.0548 | 0.9840 | |
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| 0.0272 | 2.51 | 5500 | 0.0565 | 0.9841 | |
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| 0.027 | 2.56 | 5600 | 0.0565 | 0.9840 | |
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| 0.0154 | 2.61 | 5700 | 0.0671 | 0.9818 | |
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| 0.0315 | 2.65 | 5800 | 0.0554 | 0.9851 | |
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| 0.0351 | 2.7 | 5900 | 0.0638 | 0.9832 | |
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| 0.0216 | 2.74 | 6000 | 0.0517 | 0.9851 | |
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| 0.0218 | 2.79 | 6100 | 0.0574 | 0.9844 | |
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| 0.0324 | 2.83 | 6200 | 0.0526 | 0.9851 | |
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| 0.0365 | 2.88 | 6300 | 0.0546 | 0.9852 | |
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| 0.0333 | 2.93 | 6400 | 0.0523 | 0.9854 | |
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| 0.0121 | 2.97 | 6500 | 0.0570 | 0.9845 | |
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### Framework versions |
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- Transformers 4.33.0 |
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- Pytorch 2.0.0+cu117 |
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- Datasets 2.19.1 |
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- Tokenizers 0.13.3 |
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