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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/swin-large-patch4-window7-224-in22k |
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model-index: |
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- name: blood-swin-base-finetuned-wandb |
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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-swin-base-finetuned-wandb |
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This model is a fine-tuned version of [microsoft/swin-large-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-large-patch4-window7-224-in22k) on the medmnist-v2 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1036 |
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- Accuracy: 0.9649 |
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- Precision: 0.9627 |
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- Recall: 0.9616 |
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- F1: 0.9619 |
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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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| 0.5141 | 1.0 | 187 | 0.2833 | 0.9065 | 0.8954 | 0.8949 | 0.8873 | |
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| 0.4176 | 2.0 | 374 | 0.1986 | 0.9311 | 0.9243 | 0.9209 | 0.9182 | |
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| 0.3454 | 3.0 | 561 | 0.1567 | 0.9504 | 0.9427 | 0.9397 | 0.9403 | |
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| 0.3228 | 4.0 | 748 | 0.1849 | 0.9357 | 0.9232 | 0.9426 | 0.9283 | |
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| 0.3382 | 5.0 | 935 | 0.1627 | 0.9398 | 0.9302 | 0.9397 | 0.9321 | |
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| 0.3363 | 6.0 | 1122 | 0.1414 | 0.9509 | 0.9498 | 0.9442 | 0.9456 | |
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| 0.2981 | 7.0 | 1309 | 0.1117 | 0.9544 | 0.9458 | 0.9542 | 0.9480 | |
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| 0.2214 | 8.0 | 1496 | 0.1131 | 0.9650 | 0.9642 | 0.9584 | 0.9610 | |
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| 0.1928 | 9.0 | 1683 | 0.0966 | 0.9650 | 0.9632 | 0.9628 | 0.9624 | |
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| 0.1901 | 10.0 | 1870 | 0.0775 | 0.9714 | 0.9690 | 0.9699 | 0.9692 | |
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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 |
