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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: facebook/deit-base-patch16-224 |
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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-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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# organsmnist-vit-base-finetuned |
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This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the medmnist-v2 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2964 |
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- Accuracy: 0.8993 |
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- Precision: 0.8443 |
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- Recall: 0.8396 |
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- F1: 0.8394 |
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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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| 0.9084 | 1.0 | 218 | 0.7151 | 0.7288 | 0.6998 | 0.6620 | 0.6412 | |
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| 0.89 | 2.0 | 436 | 0.3658 | 0.8540 | 0.7873 | 0.7898 | 0.7660 | |
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| 0.7851 | 3.0 | 654 | 0.3514 | 0.8438 | 0.8110 | 0.7674 | 0.7741 | |
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| 0.7144 | 4.0 | 872 | 0.3632 | 0.8670 | 0.8415 | 0.8133 | 0.7980 | |
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| 0.7383 | 5.0 | 1090 | 0.3680 | 0.8581 | 0.7769 | 0.8029 | 0.7786 | |
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| 0.6065 | 6.0 | 1308 | 0.2824 | 0.8870 | 0.8481 | 0.8328 | 0.8305 | |
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| 0.521 | 7.0 | 1526 | 0.2769 | 0.8940 | 0.8439 | 0.8404 | 0.8297 | |
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| 0.5305 | 8.0 | 1744 | 0.2611 | 0.9001 | 0.8517 | 0.8463 | 0.8447 | |
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| 0.4522 | 9.0 | 1962 | 0.2742 | 0.9058 | 0.8594 | 0.8517 | 0.8411 | |
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| 0.4445 | 10.0 | 2180 | 0.2964 | 0.8993 | 0.8443 | 0.8396 | 0.8394 | |
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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 |
