Instructions to use procodomatic/food_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use procodomatic/food_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="procodomatic/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("procodomatic/food_classifier") model = AutoModelForImageClassification.from_pretrained("procodomatic/food_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from procodomatic/food_classifier: direct link, hf CLI and curl.
- Browser
- Download file 1.69 kB
-
https://huggingface.co/procodomatic/food_classifier/resolve/main/README.md
- Command line
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hf download hf://procodomatic/food_classifier/README.md
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curl -L -o README.md https://huggingface.co/procodomatic/food_classifier/resolve/main/README.md
1.69 kB
| license: apache-2.0 | |
| base_model: google/vit-base-patch16-224-in21k | |
| tags: | |
| - generated_from_keras_callback | |
| model-index: | |
| - name: procodomatic/food_classifier | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information Keras had access to. You should | |
| probably proofread and complete it, then remove this comment. --> | |
| # procodomatic/food_classifier | |
| This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Train Loss: 4.5588 | |
| - Validation Loss: 4.4765 | |
| - Train Accuracy: 0.0 | |
| - 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': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20, '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 | Validation Loss | Train Accuracy | Epoch | | |
| |:----------:|:---------------:|:--------------:|:-----:| | |
| | 4.5588 | 4.4765 | 0.0 | 0 | | |
| ### Framework versions | |
| - Transformers 4.41.2 | |
| - TensorFlow 2.15.0 | |
| - Datasets 2.19.2 | |
| - Tokenizers 0.19.1 | |