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mostafasmart
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vit-base-patch16-224-4class224

Image Classification
Transformers
TensorFlow
vit
generated_from_keras_callback
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xet
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How to use from the
Use from the
Transformers library
# Gated model: Login with a HF token with gated access permission
hf auth login
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-classification", model="mostafasmart/vit-base-patch16-224-4class224")
pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification

processor = AutoImageProcessor.from_pretrained("mostafasmart/vit-base-patch16-224-4class224")
model = AutoModelForImageClassification.from_pretrained("mostafasmart/vit-base-patch16-224-4class224", device_map="auto")
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vit-base-patch16-224-4class224

This model is a fine-tuned version of google/vit-base-patch16-224 on an unknown dataset. It achieves the following results on the evaluation set:

  • Train Loss: 0.0136
  • Train Accuracy: 0.9421
  • Train Top-3-accuracy: 0.9958
  • Validation Loss: 0.1390
  • Validation Accuracy: 0.9458
  • Validation Top-3-accuracy: 0.9961
  • Epoch: 6

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': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 455, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
  • training_precision: float32

Training results

Train Loss Train Accuracy Train Top-3-accuracy Validation Loss Validation Accuracy Validation Top-3-accuracy Epoch
0.7231 0.5836 0.9174 0.3551 0.7352 0.9701 0
0.2208 0.8012 0.9802 0.2265 0.8400 0.9858 1
0.0854 0.8664 0.9886 0.1859 0.8862 0.9907 2
0.0372 0.8996 0.9920 0.1565 0.9111 0.9931 3
0.0212 0.9199 0.9938 0.1411 0.9272 0.9945 4
0.0167 0.9328 0.9950 0.1374 0.9379 0.9954 5
0.0136 0.9421 0.9958 0.1390 0.9458 0.9961 6

Framework versions

  • Transformers 4.41.2
  • TensorFlow 2.15.0
  • Datasets 2.20.0
  • Tokenizers 0.19.1
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