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--- |
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library_name: transformers |
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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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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: swinv2-tiny-patch4-window8-256-DMAE-4e-3 |
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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: test |
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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: 0.7391304347826086 |
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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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# swinv2-tiny-patch4-window8-256-DMAE-4e-3 |
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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 the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7507 |
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- Accuracy: 0.7391 |
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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: 3e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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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 | 0.8571 | 3 | 1.3959 | 0.3043 | |
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| No log | 1.7857 | 6 | 1.2662 | 0.3913 | |
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| No log | 2.7143 | 9 | 1.1960 | 0.4783 | |
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| 1.3226 | 3.9286 | 13 | 1.1950 | 0.4565 | |
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| 1.3226 | 4.8571 | 16 | 1.1891 | 0.4783 | |
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| 1.3226 | 5.7857 | 19 | 1.1898 | 0.4783 | |
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| 1.1833 | 6.7143 | 22 | 1.1824 | 0.5435 | |
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| 1.1833 | 7.9286 | 26 | 1.1618 | 0.5217 | |
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| 1.1833 | 8.8571 | 29 | 1.1359 | 0.5652 | |
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| 1.1384 | 9.7857 | 32 | 1.0974 | 0.5870 | |
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| 1.1384 | 10.7143 | 35 | 1.0524 | 0.5870 | |
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| 1.1384 | 11.9286 | 39 | 1.0083 | 0.6957 | |
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| 1.0628 | 12.8571 | 42 | 0.9696 | 0.6739 | |
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| 1.0628 | 13.7857 | 45 | 0.9369 | 0.6739 | |
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| 1.0628 | 14.7143 | 48 | 0.8825 | 0.7174 | |
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| 1.0069 | 15.9286 | 52 | 0.8396 | 0.6957 | |
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| 1.0069 | 16.8571 | 55 | 0.8267 | 0.7174 | |
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| 1.0069 | 17.7857 | 58 | 0.8275 | 0.7174 | |
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| 0.9339 | 18.7143 | 61 | 0.8255 | 0.7174 | |
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| 0.9339 | 19.9286 | 65 | 0.7899 | 0.7174 | |
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| 0.9339 | 20.8571 | 68 | 0.7604 | 0.7174 | |
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| 0.905 | 21.7857 | 71 | 0.7442 | 0.6957 | |
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| 0.905 | 22.7143 | 74 | 0.7361 | 0.7391 | |
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| 0.905 | 23.9286 | 78 | 0.7598 | 0.6957 | |
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| 0.8465 | 24.8571 | 81 | 0.7650 | 0.7174 | |
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| 0.8465 | 25.7857 | 84 | 0.7631 | 0.7391 | |
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| 0.8465 | 26.7143 | 87 | 0.7561 | 0.7174 | |
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| 0.8363 | 27.9286 | 91 | 0.7494 | 0.6957 | |
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| 0.8363 | 28.8571 | 94 | 0.7539 | 0.7174 | |
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| 0.8363 | 29.7857 | 97 | 0.7497 | 0.7174 | |
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| 0.7751 | 30.7143 | 100 | 0.7477 | 0.7174 | |
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| 0.7751 | 31.9286 | 104 | 0.7463 | 0.7609 | |
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| 0.7751 | 32.8571 | 107 | 0.7507 | 0.7609 | |
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| 0.7843 | 33.7857 | 110 | 0.7534 | 0.7391 | |
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| 0.7843 | 34.7143 | 113 | 0.7542 | 0.7391 | |
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| 0.7843 | 35.9286 | 117 | 0.7519 | 0.7391 | |
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| 0.7435 | 36.8571 | 120 | 0.7507 | 0.7391 | |
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### Framework versions |
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- Transformers 4.46.2 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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