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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/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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: swin-tiny-patch4-window7-224-image-classifier |
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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: 0.8080808080808081 |
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- name: F1 |
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type: f1 |
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value: 0.750428326670474 |
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- name: Precision |
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type: precision |
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value: 0.6850886339937435 |
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- name: Recall |
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type: recall |
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value: 0.8295454545454546 |
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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-image-classifier |
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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: 0.3204 |
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- Accuracy: 0.8081 |
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- F1: 0.7504 |
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- Precision: 0.6851 |
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- Recall: 0.8295 |
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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: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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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: 30 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.6377 | 1.0 | 143 | 0.6156 | 0.6421 | 0.5356 | 0.4881 | 0.5934 | |
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| 0.4556 | 2.0 | 286 | 0.4928 | 0.7141 | 0.6286 | 0.5734 | 0.6957 | |
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| 0.3616 | 3.0 | 429 | 0.5772 | 0.6895 | 0.5930 | 0.5450 | 0.6503 | |
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| 0.3582 | 4.0 | 572 | 0.3441 | 0.7835 | 0.6644 | 0.7208 | 0.6162 | |
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| 0.3374 | 5.0 | 715 | 0.4094 | 0.7699 | 0.7434 | 0.6072 | 0.9583 | |
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| 0.3273 | 6.0 | 858 | 0.6065 | 0.7115 | 0.6364 | 0.5665 | 0.7260 | |
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| 0.3091 | 7.0 | 1001 | 0.3204 | 0.8081 | 0.7504 | 0.6851 | 0.8295 | |
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| 0.3026 | 8.0 | 1144 | 0.3946 | 0.7694 | 0.6120 | 0.7380 | 0.5227 | |
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### Framework versions |
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- Transformers 4.46.2 |
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- Pytorch 2.5.0+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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