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---
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license: apache-2.0
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base_model: microsoft/beit-large-patch16-224
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tags:
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- generated_from_trainer
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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-Shuffled
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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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# beit-large-patch16-224-finetuned-Lesion-Classification-HAM10000-AH-60-20-20-Shuffled
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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 None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0487
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- Accuracy: 0.9893
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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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| 2.1055 | 1.0 | 114 | 2.0091 | 0.1601 |
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| 1.6582 | 2.0 | 229 | 1.5953 | 0.4187 |
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| 1.2399 | 3.0 | 343 | 1.1053 | 0.5977 |
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| 0.8417 | 4.0 | 458 | 0.7602 | 0.7241 |
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| 0.5517 | 5.0 | 572 | 0.5651 | 0.8013 |
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| 0.5777 | 6.0 | 687 | 0.3980 | 0.8768 |
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| 0.408 | 7.0 | 801 | 0.2912 | 0.9154 |
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| 0.2395 | 8.0 | 916 | 0.2185 | 0.9417 |
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| 0.3613 | 9.0 | 1030 | 0.1753 | 0.9475 |
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| 0.2408 | 10.0 | 1145 | 0.1353 | 0.9614 |
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| 0.2777 | 11.0 | 1259 | 0.0699 | 0.9860 |
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| 0.1528 | 11.95 | 1368 | 0.0487 | 0.9893 |
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### Framework versions
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- Transformers 4.31.0
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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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