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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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model-index: |
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- name: swin-tiny-patch4-window7-224-finetuned-phones |
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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: 0.8653846153846154 |
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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-finetuned-phones |
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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: 0.3938 |
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- Accuracy: 0.8654 |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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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| No log | 0.9333 | 7 | 0.6743 | 0.5673 | |
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| 0.6763 | 2.0 | 15 | 0.6166 | 0.6923 | |
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| 0.635 | 2.9333 | 22 | 0.5646 | 0.7404 | |
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| 0.5724 | 4.0 | 30 | 0.5074 | 0.7308 | |
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| 0.5724 | 4.9333 | 37 | 0.4809 | 0.7692 | |
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| 0.527 | 6.0 | 45 | 0.4597 | 0.7692 | |
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| 0.5304 | 6.9333 | 52 | 0.4758 | 0.7596 | |
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| 0.4597 | 8.0 | 60 | 0.4343 | 0.7885 | |
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| 0.4597 | 8.9333 | 67 | 0.4249 | 0.7981 | |
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| 0.4606 | 10.0 | 75 | 0.4236 | 0.7981 | |
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| 0.4286 | 10.9333 | 82 | 0.4055 | 0.8462 | |
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| 0.3857 | 12.0 | 90 | 0.4144 | 0.8269 | |
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| 0.3857 | 12.9333 | 97 | 0.4294 | 0.7981 | |
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| 0.3801 | 14.0 | 105 | 0.4081 | 0.8462 | |
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| 0.3538 | 14.9333 | 112 | 0.4195 | 0.8462 | |
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| 0.3585 | 16.0 | 120 | 0.4069 | 0.8558 | |
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| 0.3585 | 16.9333 | 127 | 0.3971 | 0.8558 | |
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| 0.3258 | 18.0 | 135 | 0.3938 | 0.8654 | |
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| 0.3288 | 18.9333 | 142 | 0.3964 | 0.8462 | |
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| 0.3276 | 20.0 | 150 | 0.4423 | 0.8558 | |
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| 0.3276 | 20.9333 | 157 | 0.4067 | 0.8365 | |
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| 0.317 | 22.0 | 165 | 0.4179 | 0.8654 | |
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| 0.288 | 22.9333 | 172 | 0.3882 | 0.8558 | |
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| 0.2735 | 24.0 | 180 | 0.4215 | 0.8558 | |
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| 0.2735 | 24.9333 | 187 | 0.3972 | 0.8462 | |
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| 0.2805 | 26.0 | 195 | 0.3943 | 0.8558 | |
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| 0.2961 | 26.9333 | 202 | 0.3999 | 0.8558 | |
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| 0.2832 | 28.0 | 210 | 0.4043 | 0.8558 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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