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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: organsmnist-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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# organsmnist-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: 0.4609 |
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- Accuracy: 0.8240 |
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- Precision: 0.7895 |
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- Recall: 0.7821 |
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- F1: 0.7852 |
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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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| 0.9608 | 1.0 | 218 | 0.6055 | 0.7765 | 0.7235 | 0.7233 | 0.7007 | |
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| 0.9984 | 2.0 | 436 | 0.4812 | 0.8067 | 0.7265 | 0.7321 | 0.7114 | |
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| 0.8265 | 3.0 | 654 | 0.3726 | 0.8520 | 0.8005 | 0.7713 | 0.7683 | |
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| 0.7938 | 4.0 | 872 | 0.3913 | 0.8507 | 0.7812 | 0.7831 | 0.7554 | |
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| 0.8149 | 5.0 | 1090 | 0.3676 | 0.8532 | 0.7687 | 0.8002 | 0.7702 | |
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| 0.6737 | 6.0 | 1308 | 0.3305 | 0.8675 | 0.8306 | 0.8117 | 0.7934 | |
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| 0.5695 | 7.0 | 1526 | 0.2481 | 0.9029 | 0.8546 | 0.8469 | 0.8321 | |
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| 0.5857 | 8.0 | 1744 | 0.2912 | 0.8923 | 0.8464 | 0.8356 | 0.8340 | |
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| 0.4834 | 9.0 | 1962 | 0.2658 | 0.8997 | 0.8428 | 0.8410 | 0.8286 | |
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| 0.5287 | 10.0 | 2180 | 0.2590 | 0.9050 | 0.8524 | 0.8468 | 0.8468 | |
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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 |
