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---
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library_name: transformers
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license: apache-2.0
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base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
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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-base-patch16-224-pt22k-ft22k-finetuned-ISIC-dec2024new
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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: default
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split: train
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args: default
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.9404796301646923
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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-base-patch16-224-pt22k-ft22k-finetuned-ISIC-dec2024new
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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 imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1463
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- Accuracy: 0.9405
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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-05
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 128
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 5
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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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| 0.2051 | 0.9985 | 486 | 0.1758 | 0.9276 |
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| 0.169 | 1.9985 | 972 | 0.1703 | 0.9312 |
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| 0.1572 | 2.9985 | 1458 | 0.1675 | 0.9296 |
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| 0.1572 | 3.9985 | 1944 | 0.1472 | 0.9385 |
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| 0.1362 | 4.9985 | 2430 | 0.1463 | 0.9405 |
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### Framework versions
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- Transformers 4.48.0
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- Pytorch 2.7.0.dev20250117+cu118
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- Datasets 3.2.0
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- Tokenizers 0.21.0
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