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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: google/vit-base-patch16-224-in21k |
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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: pneumoniamnist-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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# pneumoniamnist-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: 0.3312 |
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- Accuracy: 0.8878 |
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- Precision: 0.9217 |
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- Recall: 0.8513 |
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- F1: 0.8712 |
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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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| 0.2447 | 0.9898 | 73 | 0.1180 | 0.9561 | 0.9313 | 0.9608 | 0.9446 | |
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| 0.2136 | 1.9932 | 147 | 0.1015 | 0.9637 | 0.9498 | 0.9562 | 0.9529 | |
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| 0.1431 | 2.9966 | 221 | 0.0729 | 0.9752 | 0.9732 | 0.9615 | 0.9672 | |
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| 0.1576 | 4.0 | 295 | 0.0873 | 0.9637 | 0.9480 | 0.9586 | 0.9532 | |
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| 0.2072 | 4.9898 | 368 | 0.0761 | 0.9714 | 0.9616 | 0.9638 | 0.9627 | |
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| 0.1908 | 5.9932 | 442 | 0.1044 | 0.9599 | 0.9348 | 0.9682 | 0.9496 | |
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| 0.1637 | 6.9966 | 516 | 0.0742 | 0.9676 | 0.9512 | 0.9661 | 0.9583 | |
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| 0.1385 | 8.0 | 590 | 0.1843 | 0.9313 | 0.8947 | 0.9537 | 0.9169 | |
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| 0.1335 | 8.9898 | 663 | 0.0677 | 0.9752 | 0.9626 | 0.9736 | 0.9680 | |
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| 0.1186 | 9.8983 | 730 | 0.0765 | 0.9752 | 0.9626 | 0.9736 | 0.9680 | |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.41.1 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
