README.md

metadata

library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - image-classification
  - tensorflow
  - vision
  - generated_from_keras_callback
model-index:
  - name: RenSurii/vit-base-patch16-224-in21k-finetuned-image-classification
    results: []

RenSurii/vit-base-patch16-224-in21k-finetuned-image-classification

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:

• Train Loss: 2.0107
• Train Accuracy: 0.8548
• Validation Loss: 1.5288
• Validation Accuracy: 0.8548
• Epoch: 0

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:

• optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': 1.0, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 6000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
• training_precision: float32

Training results

Train Loss	Train Accuracy	Validation Loss	Validation Accuracy	Epoch
2.0107	0.8548	1.5288	0.8548	0

Framework versions

• Transformers 4.47.0.dev0
• TensorFlow 2.18.0
• Datasets 3.1.0
• Tokenizers 0.20.3