metadata
license: apache-2.0
base_model: facebook/vit-msn-small
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-msn-small-finetuned-alzheimers
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: 0.8625
vit-msn-small-finetuned-alzheimers
This model is a fine-tuned version of facebook/vit-msn-small on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.3612
- Accuracy: 0.8625
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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- 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 |
|---|---|---|---|---|
| 0.9297 | 0.9778 | 22 | 0.8769 | 0.6156 |
| 0.8601 | 2.0 | 45 | 0.7799 | 0.6344 |
| 0.7954 | 2.9778 | 67 | 0.7197 | 0.6828 |
| 0.7468 | 4.0 | 90 | 0.7003 | 0.6734 |
| 0.6935 | 4.9778 | 112 | 0.6064 | 0.7547 |
| 0.6271 | 6.0 | 135 | 0.5648 | 0.7688 |
| 0.5622 | 6.9778 | 157 | 0.4824 | 0.8094 |
| 0.4815 | 8.0 | 180 | 0.4012 | 0.8609 |
| 0.4771 | 8.9778 | 202 | 0.3799 | 0.8562 |
| 0.4171 | 9.7778 | 220 | 0.3612 | 0.8625 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1