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