metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-ecg
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9642857142857143
vit-base-ecg
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: 0.1003
- Accuracy: 0.9643
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: 0.0002
- 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
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.596 | 2.4390 | 100 | 0.5431 | 0.8214 |
| 0.0656 | 4.8780 | 200 | 0.1628 | 0.95 |
| 0.0192 | 7.3171 | 300 | 0.1003 | 0.9643 |
| 0.0926 | 9.7561 | 400 | 0.1262 | 0.95 |
| 0.0064 | 12.1951 | 500 | 0.1611 | 0.9643 |
| 0.0049 | 14.6341 | 600 | 0.1539 | 0.9643 |
| 0.0044 | 17.0732 | 700 | 0.1509 | 0.9643 |
| 0.0041 | 19.5122 | 800 | 0.1499 | 0.9643 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1