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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: microsoft/beit-base-patch16-224-pt22k-ft22k |
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model-index: |
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- name: derma-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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# derma-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: 0.6240 |
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- Accuracy: 0.7561 |
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- Precision: 0.5742 |
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- Recall: 0.5353 |
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- F1: 0.5271 |
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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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| 0.9135 | 1.0 | 109 | 0.7698 | 0.7198 | 0.5179 | 0.3103 | 0.3050 | |
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| 0.8352 | 2.0 | 219 | 0.7352 | 0.7298 | 0.5362 | 0.4231 | 0.3884 | |
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| 0.7891 | 3.0 | 328 | 0.7575 | 0.7178 | 0.3954 | 0.4000 | 0.3667 | |
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| 0.7649 | 4.0 | 438 | 0.6879 | 0.7418 | 0.5009 | 0.3972 | 0.4146 | |
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| 0.8146 | 5.0 | 547 | 0.7471 | 0.7178 | 0.4490 | 0.4141 | 0.3641 | |
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| 0.6831 | 6.0 | 657 | 0.7007 | 0.7368 | 0.4777 | 0.4148 | 0.4252 | |
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| 0.695 | 7.0 | 766 | 0.6797 | 0.7428 | 0.4638 | 0.5334 | 0.4841 | |
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| 0.6646 | 8.0 | 876 | 0.6534 | 0.7537 | 0.6130 | 0.5077 | 0.4933 | |
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| 0.675 | 9.0 | 985 | 0.6238 | 0.7667 | 0.6518 | 0.5431 | 0.5308 | |
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| 0.6145 | 9.95 | 1090 | 0.6096 | 0.7727 | 0.6427 | 0.5346 | 0.5283 | |
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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 |
