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README.md
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---
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license: apache-2.0
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library_name: peft
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tags:
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- generated_from_trainer
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datasets:
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- medmnist-v2
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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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base_model: google/vit-base-patch16-224-in21k
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model-index:
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- name: blood-vit-base-finetuned
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results: []
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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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# blood-vit-base-finetuned
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the medmnist-v2 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0627
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- Accuracy: 0.9790
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- Precision: 0.9764
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- Recall: 0.9812
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- F1: 0.9786
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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: 0.005
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 64
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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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- num_epochs: 10
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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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| 0.4059 | 1.0 | 187 | 0.1878 | 0.9311 | 0.9132 | 0.9328 | 0.9201 |
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| 0.3796 | 2.0 | 374 | 0.2729 | 0.9083 | 0.9131 | 0.8875 | 0.8861 |
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| 0.424 | 3.0 | 561 | 0.3701 | 0.8668 | 0.8797 | 0.8520 | 0.8492 |
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| 0.3141 | 4.0 | 748 | 0.1849 | 0.9381 | 0.9267 | 0.9336 | 0.9283 |
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| 0.2553 | 5.0 | 935 | 0.1075 | 0.9644 | 0.9630 | 0.9612 | 0.9617 |
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| 0.2686 | 6.0 | 1122 | 0.1679 | 0.9486 | 0.9561 | 0.9437 | 0.9489 |
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| 0.2556 | 7.0 | 1309 | 0.0934 | 0.9661 | 0.9651 | 0.9599 | 0.9619 |
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| 0.1777 | 8.0 | 1496 | 0.0835 | 0.9696 | 0.9697 | 0.9683 | 0.9686 |
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| 0.1607 | 9.0 | 1683 | 0.0739 | 0.9772 | 0.9733 | 0.9792 | 0.9759 |
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| 0.1898 | 10.0 | 1870 | 0.0627 | 0.9790 | 0.9764 | 0.9812 | 0.9786 |
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### Framework versions
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- PEFT 0.9.0
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- Transformers 4.38.2
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- Pytorch 2.2.1+cu121
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- Datasets 2.18.0
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- Tokenizers 0.15.2
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