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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: 0.0847 |
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- Accuracy: 0.9737 |
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- Precision: 0.9726 |
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- Recall: 0.9724 |
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- F1: 0.9724 |
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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 |
