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vit-base-patch16-224-in21k-finetuned-image-classification – AI Model by dhanesh123in | AlphaNeural AI
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dhanesh123in
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vit-base-patch16-224-in21k-finetuned-image-classification
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transformers
tf
vit
image-classification
tensorflow
vision
generated_from_keras_callback
google/vit-base-patch16-224-in21k
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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dhanesh123in/vit-base-patch16-224-in21k-finetuned-image-classification
This model is a fine-tuned version of
google/vit-base-patch16-224-in21k
on the beans dataset. It achieves the following results on the evaluation set:
Train Loss: 0.7983
Train Accuracy: 0.9624
Validation Loss: 0.4438
Validation Accuracy: 0.9624
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': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 5170, '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
0.7983
0.9624
0.4438
0.9624
0
Framework versions
Transformers 4.36.0.dev0
TensorFlow 2.15.0
Datasets 2.15.0
Tokenizers 0.15.0