Instructions to use mLStudent33/food_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mLStudent33/food_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mLStudent33/food_classifier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("mLStudent33/food_classifier") model = AutoModelForImageClassification.from_pretrained("mLStudent33/food_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from mLStudent33/food_classifier: direct link, hf CLI and curl.
- Browser
- Download file 1.81 kB
-
https://huggingface.co/mLStudent33/food_classifier/resolve/main/README.md
- Command line
-
hf download hf://mLStudent33/food_classifier/README.md
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curl -L -o README.md https://huggingface.co/mLStudent33/food_classifier/resolve/main/README.md
1.81 kB
metadata
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: mLStudent33/food_classifier
results: []
mLStudent33/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: 0.3593
- Validation Loss: 0.3743
- Train Accuracy: 0.904
- 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': 20000, '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 |
|---|---|---|---|
| 2.7796 | 1.6388 | 0.813 | 0 |
| 1.2116 | 0.8187 | 0.893 | 1 |
| 0.6682 | 0.5495 | 0.892 | 2 |
| 0.4887 | 0.4037 | 0.908 | 3 |
| 0.3593 | 0.3743 | 0.904 | 4 |
Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3