metadata
license: apache-2.0
tags:
- image-classification
- vision
- generated_from_trainer
datasets:
- mnist
metrics:
- accuracy
base_model: google/vit-base-patch16-224-in21k
model-index:
- name: vit-base-mnist
results:
- task:
type: image-classification
name: Image Classification
dataset:
name: mnist
type: mnist
config: mnist
split: train
args: mnist
metrics:
- type: accuracy
value: 0.9948888888888889
name: Accuracy
vit-base-mnist
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the mnist dataset. It achieves the following results on the evaluation set:
- Loss: 0.0236
- Accuracy: 0.9949
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.3717 | 1.0 | 6375 | 0.0522 | 0.9893 |
| 0.3453 | 2.0 | 12750 | 0.0370 | 0.9906 |
| 0.3736 | 3.0 | 19125 | 0.0308 | 0.9916 |
| 0.3224 | 4.0 | 25500 | 0.0269 | 0.9939 |
| 0.2846 | 5.0 | 31875 | 0.0236 | 0.9949 |
Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.11.0a0+17540c5
- Datasets 2.4.0
- Tokenizers 0.12.1