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--- |
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license: apache-2.0 |
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base_model: microsoft/swinv2-base-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-base-patch4-window8-256 |
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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: train |
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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: 0.7241379310344828 |
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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-base-patch4-window8-256 |
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This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window8-256](https://huggingface.co/microsoft/swinv2-base-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: 0.6211 |
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- Accuracy: 0.7241 |
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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: 0.0001 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 4 |
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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: 30 |
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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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| 0.5428 | 0.9912 | 28 | 0.6211 | 0.7241 | |
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| 0.6494 | 1.9823 | 56 | 0.6130 | 0.7241 | |
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| 0.5752 | 2.9735 | 84 | 0.6846 | 0.7241 | |
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| 0.7165 | 4.0 | 113 | 0.9642 | 0.7241 | |
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| 0.5699 | 4.9912 | 141 | 0.6072 | 0.7241 | |
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| 0.5517 | 5.9823 | 169 | 0.6231 | 0.7241 | |
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| 0.5268 | 6.9735 | 197 | 0.6098 | 0.7241 | |
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| 0.672 | 8.0 | 226 | 0.5891 | 0.7241 | |
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| 0.5448 | 8.9912 | 254 | 0.6023 | 0.7241 | |
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| 0.555 | 9.9823 | 282 | 0.5917 | 0.7241 | |
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| 0.5818 | 10.9735 | 310 | 0.5940 | 0.7241 | |
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| 0.6556 | 12.0 | 339 | 0.5966 | 0.7241 | |
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| 0.716 | 12.9912 | 367 | 0.5904 | 0.7241 | |
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| 0.6104 | 13.9823 | 395 | 0.5938 | 0.7241 | |
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| 0.5046 | 14.9735 | 423 | 0.5921 | 0.7241 | |
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| 0.5871 | 16.0 | 452 | 0.6027 | 0.7241 | |
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| 0.5222 | 16.9912 | 480 | 0.5921 | 0.7241 | |
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| 0.5511 | 17.9823 | 508 | 0.5948 | 0.7241 | |
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| 0.6394 | 18.9735 | 536 | 0.5969 | 0.7241 | |
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| 0.566 | 20.0 | 565 | 0.6005 | 0.7241 | |
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| 0.6032 | 20.9912 | 593 | 0.5968 | 0.7241 | |
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| 0.4824 | 21.9823 | 621 | 0.5934 | 0.7241 | |
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| 0.4975 | 22.9735 | 649 | 0.5979 | 0.7241 | |
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| 0.4976 | 24.0 | 678 | 0.6034 | 0.7241 | |
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| 0.5355 | 24.9912 | 706 | 0.6033 | 0.7241 | |
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| 0.4323 | 25.9823 | 734 | 0.6015 | 0.7241 | |
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| 0.5579 | 26.9735 | 762 | 0.6043 | 0.7241 | |
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| 0.5639 | 28.0 | 791 | 0.6023 | 0.7241 | |
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| 0.5595 | 28.9912 | 819 | 0.5996 | 0.7241 | |
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| 0.4372 | 29.7345 | 840 | 0.5995 | 0.7241 | |
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### Framework versions |
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- Transformers 4.42.3 |
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- Pytorch 2.3.1+cu118 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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