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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: google/vit-large-patch16-224 |
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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: vit_itri_gerd |
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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: 0.8802395209580839 |
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- name: Precision |
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type: precision |
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value: 0.8810801871515888 |
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- name: Recall |
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type: recall |
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value: 0.8802395209580839 |
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- name: F1 |
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type: f1 |
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value: 0.8801535602352574 |
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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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# vit_itri_gerd |
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This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8160 |
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- Accuracy: 0.8802 |
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- Precision: 0.8811 |
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- Recall: 0.8802 |
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- F1: 0.8802 |
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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: 24 |
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- eval_batch_size: 4 |
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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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- num_epochs: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.6915 | 1.0 | 63 | 0.4305 | 0.7904 | 0.7926 | 0.7904 | 0.7901 | |
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| 0.455 | 2.0 | 126 | 0.7307 | 0.7605 | 0.7836 | 0.7605 | 0.7552 | |
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| 0.372 | 3.0 | 189 | 0.4026 | 0.8024 | 0.8123 | 0.8024 | 0.8007 | |
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| 0.3159 | 4.0 | 252 | 0.3805 | 0.8323 | 0.8340 | 0.8323 | 0.8321 | |
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| 0.2906 | 5.0 | 315 | 0.4334 | 0.8323 | 0.8326 | 0.8323 | 0.8323 | |
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| 0.2589 | 6.0 | 378 | 0.4235 | 0.8084 | 0.8232 | 0.8084 | 0.8060 | |
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| 0.2024 | 7.0 | 441 | 0.4003 | 0.8503 | 0.8516 | 0.8503 | 0.8502 | |
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| 0.1218 | 8.0 | 504 | 0.6308 | 0.8204 | 0.8270 | 0.8204 | 0.8193 | |
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| 0.1226 | 9.0 | 567 | 0.5468 | 0.8323 | 0.8353 | 0.8323 | 0.8319 | |
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| 0.0627 | 10.0 | 630 | 0.7390 | 0.8263 | 0.8286 | 0.8263 | 0.8260 | |
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| 0.0374 | 11.0 | 693 | 0.8669 | 0.8503 | 0.8503 | 0.8503 | 0.8503 | |
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| 0.0389 | 12.0 | 756 | 0.6790 | 0.8623 | 0.8627 | 0.8623 | 0.8622 | |
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| 0.0122 | 13.0 | 819 | 0.8346 | 0.8683 | 0.8701 | 0.8683 | 0.8681 | |
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| 0.0064 | 14.0 | 882 | 0.7985 | 0.8802 | 0.8804 | 0.8802 | 0.8802 | |
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| 0.0071 | 15.0 | 945 | 0.8160 | 0.8802 | 0.8811 | 0.8802 | 0.8802 | |
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### Framework versions |
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- Transformers 4.53.0.dev0 |
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- Pytorch 2.7.1+cu126 |
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- Datasets 3.6.0 |
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- Tokenizers 0.21.1 |
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