metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-brain-alzheimer-detection
results: []
vit-base-brain-alzheimer-detection
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2301
- Accuracy: 0.9555
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4285 | 1.9531 | 500 | 0.4633 | 0.8311 |
| 0.171 | 3.9062 | 1000 | 0.3237 | 0.8994 |
| 0.0622 | 5.8594 | 1500 | 0.2032 | 0.9414 |
| 0.0162 | 7.8125 | 2000 | 0.2413 | 0.9512 |
| 0.0044 | 9.7656 | 2500 | 0.1623 | 0.9668 |
| 0.003 | 11.7188 | 3000 | 0.1641 | 0.9668 |
| 0.0025 | 13.6719 | 3500 | 0.1796 | 0.9619 |
| 0.0019 | 15.625 | 4000 | 0.1892 | 0.9590 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1