# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("CharlesBointon/food_classifier")
model = AutoModelForImageClassification.from_pretrained("CharlesBointon/food_classifier")Quick Links
CharlesBointon/food_classifier
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 1.2662
- Validation Loss: 1.1840
- Train Accuracy: 1.0
- Epoch: 4
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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 2000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, '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 | Validation Loss | Train Accuracy | Epoch |
|---|---|---|---|
| 3.6773 | 2.6774 | 1.0 | 0 |
| 2.1700 | 1.8405 | 1.0 | 1 |
| 1.6777 | 1.5476 | 1.0 | 2 |
| 1.4452 | 1.3513 | 1.0 | 3 |
| 1.2662 | 1.1840 | 1.0 | 4 |
Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.9.1
- Datasets 2.14.2
- Tokenizers 0.12.1
- Downloads last month
- 7
Model tree for CharlesBointon/food_classifier
Base model
google/vit-base-patch16-224-in21k
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="CharlesBointon/food_classifier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")