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Alzheimer_classification_model
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.4065
- Accuracy: 0.8375
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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.692 | 1.0 | 80 | 0.8592 | 0.6258 |
| 0.662 | 2.0 | 160 | 0.7454 | 0.6781 |
| 0.6124 | 3.0 | 240 | 0.6895 | 0.6922 |
| 0.5851 | 4.0 | 320 | 0.6332 | 0.7430 |
| 0.5495 | 5.0 | 400 | 0.5804 | 0.7586 |
| 0.4334 | 6.0 | 480 | 0.6068 | 0.7484 |
| 0.4169 | 7.0 | 560 | 0.5168 | 0.7883 |
| 0.3709 | 8.0 | 640 | 0.4768 | 0.8055 |
| 0.2854 | 9.0 | 720 | 0.4641 | 0.8117 |
| 0.3064 | 10.0 | 800 | 0.4065 | 0.8375 |
Framework versions
- Transformers 4.27.1
- Pytorch 2.0.1+cu118
- Datasets 2.9.0
- Tokenizers 0.13.3
