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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- imagefolder
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metrics:
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- accuracy
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model-index:
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- name: beit-large-patch16-224-finetuned-Lesion-Classification-HAM10000-AH-60-20-20
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results:
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- task:
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name: Image Classification
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type: image-classification
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dataset:
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name: imagefolder
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type: imagefolder
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config: Augmented-Final
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split: train
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args: Augmented-Final
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.9907502569373073
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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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# beit-large-patch16-224-finetuned-Lesion-Classification-HAM10000-AH-60-20-20
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This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0434
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- Accuracy: 0.9908
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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: 5e-06
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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: 2
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- total_train_batch_size: 32
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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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- lr_scheduler_warmup_ratio: 0.9
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- num_epochs: 12
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 1.9688 | 1.0 | 122 | 1.8425 | 0.2775 |
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| 1.4822 | 2.0 | 244 | 1.3833 | 0.5457 |
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| 1.1239 | 3.0 | 366 | 0.9321 | 0.6680 |
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| 0.8686 | 4.0 | 488 | 0.6691 | 0.7698 |
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| 0.5234 | 5.0 | 610 | 0.4872 | 0.8335 |
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| 0.5246 | 6.0 | 732 | 0.3586 | 0.8736 |
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| 0.3691 | 7.0 | 854 | 0.3134 | 0.8993 |
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| 0.4708 | 8.0 | 976 | 0.2069 | 0.9394 |
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| 0.1694 | 9.0 | 1098 | 0.1832 | 0.9414 |
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| 0.2749 | 10.0 | 1220 | 0.1198 | 0.9640 |
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| 0.1777 | 11.0 | 1342 | 0.0845 | 0.9733 |
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| 0.1529 | 12.0 | 1464 | 0.0434 | 0.9908 |
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### Framework versions
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- Transformers 4.30.2
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- Pytorch 2.0.1+cu118
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- Datasets 2.13.1
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- Tokenizers 0.13.3
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