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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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base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
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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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model-index:
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- name: blood-beit-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-beit-base-finetuned
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This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the medmnist-v2 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0785
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- Accuracy: 0.9708
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- Precision: 0.9668
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- Recall: 0.9737
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- F1: 0.9698
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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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| 0.4657 | 1.0 | 187 | 0.2452 | 0.9095 | 0.8964 | 0.9083 | 0.8973 |
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| 0.4327 | 2.0 | 374 | 0.2111 | 0.9182 | 0.9299 | 0.8921 | 0.9007 |
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| 0.3977 | 3.0 | 561 | 0.1743 | 0.9340 | 0.9229 | 0.9282 | 0.9244 |
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| 0.3318 | 4.0 | 748 | 0.1776 | 0.9352 | 0.9248 | 0.9353 | 0.9285 |
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| 0.3461 | 5.0 | 935 | 0.1703 | 0.9381 | 0.9311 | 0.9344 | 0.9305 |
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| 0.3309 | 6.0 | 1122 | 0.1956 | 0.9369 | 0.9336 | 0.9397 | 0.9335 |
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| 0.3088 | 7.0 | 1309 | 0.1179 | 0.9533 | 0.9427 | 0.9525 | 0.9461 |
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| 0.2129 | 8.0 | 1496 | 0.0992 | 0.9638 | 0.9569 | 0.9674 | 0.9611 |
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| 0.2049 | 9.0 | 1683 | 0.0847 | 0.9679 | 0.9627 | 0.9683 | 0.9651 |
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| 0.2007 | 10.0 | 1870 | 0.0785 | 0.9708 | 0.9668 | 0.9737 | 0.9698 |
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### Framework versions
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- PEFT 0.11.1
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- Transformers 4.40.2
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- Pytorch 2.2.1+cu121
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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