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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-RD-da-colab |
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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: 0.5036363636363637 |
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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-RD-da-colab |
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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: 10.2420 |
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- Accuracy: 0.5036 |
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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: 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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- 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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| 0.1347 | 1.0 | 96 | 10.0386 | 0.4982 | |
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| 0.1556 | 2.0 | 192 | 9.5018 | 0.5 | |
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| 0.1051 | 3.0 | 288 | 9.9516 | 0.4982 | |
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| 0.1154 | 4.0 | 384 | 10.8351 | 0.4945 | |
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| 0.0909 | 5.0 | 480 | 11.6091 | 0.4945 | |
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| 0.0923 | 6.0 | 576 | 9.0530 | 0.5 | |
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| 0.1089 | 7.0 | 672 | 11.6765 | 0.4927 | |
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| 0.0959 | 8.0 | 768 | 11.5132 | 0.4982 | |
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| 0.1266 | 9.0 | 864 | 10.2420 | 0.5036 | |
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| 0.106 | 10.0 | 960 | 11.1262 | 0.4945 | |
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| 0.0831 | 11.0 | 1056 | 11.5815 | 0.4964 | |
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| 0.0819 | 12.0 | 1152 | 11.6394 | 0.4964 | |
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| 0.0862 | 13.0 | 1248 | 10.9660 | 0.4982 | |
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| 0.0754 | 14.0 | 1344 | 9.5463 | 0.4982 | |
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| 0.06 | 15.0 | 1440 | 10.2678 | 0.4964 | |
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| 0.0828 | 16.0 | 1536 | 11.4973 | 0.4927 | |
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| 0.0675 | 17.0 | 1632 | 10.5019 | 0.4964 | |
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| 0.0687 | 18.0 | 1728 | 10.6483 | 0.4982 | |
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| 0.0548 | 19.0 | 1824 | 11.2166 | 0.4964 | |
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| 0.0658 | 20.0 | 1920 | 11.5459 | 0.4945 | |
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| 0.0565 | 21.0 | 2016 | 11.5899 | 0.4945 | |
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| 0.0807 | 22.0 | 2112 | 10.7066 | 0.5 | |
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| 0.0289 | 23.0 | 2208 | 10.6253 | 0.4982 | |
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| 0.0755 | 24.0 | 2304 | 10.4856 | 0.5018 | |
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| 0.0483 | 25.0 | 2400 | 11.3838 | 0.4964 | |
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| 0.0732 | 26.0 | 2496 | 11.1971 | 0.4927 | |
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| 0.1424 | 27.0 | 2592 | 11.4581 | 0.4945 | |
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| 0.0814 | 28.0 | 2688 | 11.3341 | 0.4945 | |
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| 0.101 | 29.0 | 2784 | 11.5705 | 0.4927 | |
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| 0.0894 | 30.0 | 2880 | 11.5259 | 0.4927 | |
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| 0.0707 | 31.0 | 2976 | 11.1753 | 0.4945 | |
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| 0.1289 | 32.0 | 3072 | 10.5668 | 0.4964 | |
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| 0.0991 | 33.0 | 3168 | 11.1013 | 0.4945 | |
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| 0.0615 | 34.0 | 3264 | 11.0973 | 0.4945 | |
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| 0.0784 | 35.0 | 3360 | 11.0716 | 0.4945 | |
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| 0.0792 | 36.0 | 3456 | 11.0241 | 0.4945 | |
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| 0.1032 | 37.0 | 3552 | 11.2338 | 0.4945 | |
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| 0.0837 | 38.0 | 3648 | 11.4256 | 0.4927 | |
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| 0.0722 | 39.0 | 3744 | 11.3971 | 0.4945 | |
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| 0.079 | 40.0 | 3840 | 11.3523 | 0.4945 | |
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### Framework versions |
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- Transformers 4.46.3 |
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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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