metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: alzheimer-image-classification-google-vit-base-patch16
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: 0.9261006289308176
pipeline_tag: image-classification
alzheimer-image-classification-google-vit-base-patch16
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: 0.2127
- Accuracy: 0.9261
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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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 |
|---|---|---|---|---|
| 0.8167 | 1.0 | 715 | 0.7520 | 0.6494 |
| 0.6264 | 2.0 | 1431 | 0.6467 | 0.7091 |
| 0.5003 | 3.0 | 2146 | 0.5430 | 0.7594 |
| 0.3543 | 4.0 | 2862 | 0.4372 | 0.8145 |
| 0.3816 | 5.0 | 3577 | 0.3681 | 0.8428 |
| 0.2055 | 6.0 | 4293 | 0.3746 | 0.8514 |
| 0.2526 | 7.0 | 5008 | 0.2836 | 0.8907 |
| 0.1262 | 8.0 | 5724 | 0.2798 | 0.8954 |
| 0.1332 | 9.0 | 6439 | 0.2301 | 0.9159 |
| 0.0702 | 9.99 | 7150 | 0.2127 | 0.9261 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1
- Datasets 2.14.3
- Tokenizers 0.13.3