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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: organamnist-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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# organamnist-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: 0.0450 |
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- Accuracy: 0.9831 |
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- Precision: 0.9880 |
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- Recall: 0.9862 |
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- F1: 0.9869 |
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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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| 0.6786 | 1.0 | 540 | 0.1776 | 0.9339 | 0.9507 | 0.9341 | 0.9385 | |
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| 0.7397 | 2.0 | 1081 | 0.1783 | 0.9407 | 0.9539 | 0.9346 | 0.9415 | |
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| 0.7151 | 3.0 | 1621 | 0.1297 | 0.9552 | 0.9611 | 0.9555 | 0.9572 | |
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| 0.4964 | 4.0 | 2162 | 0.0741 | 0.9735 | 0.9765 | 0.9702 | 0.9730 | |
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| 0.5509 | 5.0 | 2702 | 0.0671 | 0.9770 | 0.9776 | 0.9796 | 0.9783 | |
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| 0.5746 | 6.0 | 3243 | 0.0642 | 0.9754 | 0.9810 | 0.9788 | 0.9795 | |
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| 0.4066 | 7.0 | 3783 | 0.1196 | 0.9566 | 0.9693 | 0.9563 | 0.9614 | |
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| 0.4046 | 8.0 | 4324 | 0.0469 | 0.9798 | 0.9853 | 0.9821 | 0.9834 | |
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| 0.3314 | 9.0 | 4864 | 0.0388 | 0.9861 | 0.9892 | 0.9860 | 0.9874 | |
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| 0.2865 | 9.99 | 5400 | 0.0450 | 0.9831 | 0.9880 | 0.9862 | 0.9869 | |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
