metadata
tags:
- image-classification
- generated_from_trainer
datasets:
- cifar10
metrics:
- accuracy
model-index:
- name: vit_cifar10_classification_tmp
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: cifar10
type: cifar10
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.9781
vit_cifar10_classification_tmp
This model is a fine-tuned version of againeureka/vit_cifar10_classification_tmp on the cifar10 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0945
- Accuracy: 0.9781
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: 128
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2199 | 0.26 | 100 | 0.1853 | 0.9678 |
| 0.0999 | 0.51 | 200 | 0.1270 | 0.9713 |
| 0.0944 | 0.77 | 300 | 0.0945 | 0.9781 |
Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.2