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---
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library_name: transformers
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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-finetune-kidney-stone-Jonathan_El-Beze_-w256_1k_v1-_SUR
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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: test
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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: 0.8916666666666667
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- name: Precision
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type: precision
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value: 0.8912400695750242
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- name: Recall
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type: recall
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value: 0.8916666666666667
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- name: F1
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type: f1
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value: 0.8897432083705339
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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-finetune-kidney-stone-Jonathan_El-Beze_-w256_1k_v1-_SUR
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This model was trained from scratch on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3726
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- Accuracy: 0.8917
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- Precision: 0.8912
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- Recall: 0.8917
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- F1: 0.8897
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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: 0.0002
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- train_batch_size: 32
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- eval_batch_size: 8
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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: 1
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- mixed_precision_training: Native AMP
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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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| 0.0863 | 0.6667 | 100 | 0.3726 | 0.8917 | 0.8912 | 0.8917 | 0.8897 |
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### Framework versions
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- Transformers 4.48.2
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- Pytorch 2.6.0+cu126
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- Datasets 3.2.0
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- Tokenizers 0.21.0
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