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organa-beit-base-finetuned
This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on the medmnist-v2 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1994
- Accuracy: 0.9370
- Precision: 0.9437
- Recall: 0.9408
- F1: 0.9407
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
---|---|---|---|---|---|---|---|
0.7439 | 1.0 | 324 | 0.1246 | 0.9553 | 0.9676 | 0.9501 | 0.9554 |
0.6057 | 2.0 | 648 | 0.0829 | 0.9753 | 0.9780 | 0.9802 | 0.9785 |
0.6335 | 3.0 | 972 | 0.0776 | 0.9753 | 0.9765 | 0.9759 | 0.9752 |
0.5801 | 4.0 | 1297 | 0.1233 | 0.9599 | 0.9700 | 0.9614 | 0.9645 |
0.4697 | 5.0 | 1621 | 0.1025 | 0.9615 | 0.9747 | 0.9649 | 0.9672 |
0.4513 | 6.0 | 1945 | 0.0514 | 0.9815 | 0.9821 | 0.9819 | 0.9815 |
0.4136 | 7.0 | 2269 | 0.0250 | 0.9892 | 0.9879 | 0.9906 | 0.9891 |
0.4051 | 8.0 | 2594 | 0.0288 | 0.9877 | 0.9892 | 0.9890 | 0.9888 |
0.3234 | 9.0 | 2918 | 0.0373 | 0.9892 | 0.9931 | 0.9912 | 0.9920 |
0.2806 | 9.99 | 3240 | 0.0334 | 0.9892 | 0.9931 | 0.9912 | 0.9920 |
Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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