Instructions to use Recompense/FoodVision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Recompense/FoodVision with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Recompense/FoodVision") - Notebooks
- Google Colab
- Kaggle
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README.md
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This model is a deep learning model for classifying food images into one of 101 categories from the Food101 dataset. It was trained using TensorFlow and likely employs a transfer learning approach, leveraging the features learned by a model pre-trained on a large dataset like ImageNet. The training process included the use of mixed precision for potentially faster training and reduced memory usage.
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<h1 style="color:#FFD700; font-weight: bold;">🍽️ Model Card for Food Vision Model</h1>
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<p><a href="https://
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* **Developed by:** `Recompense` Me!
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* **Model type:** Image Classification (Transfer Learning with a CNN backbone)
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This model is a deep learning model for classifying food images into one of 101 categories from the Food101 dataset. It was trained using TensorFlow and likely employs a transfer learning approach, leveraging the features learned by a model pre-trained on a large dataset like ImageNet. The training process included the use of mixed precision for potentially faster training and reduced memory usage.
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<h1 style="color:#FFD700; font-weight: bold;">🍽️ Model Card for Food Vision Model</h1>
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<p><a href="https://huggingface.co/spaces/Recompense/FoodVision" style="color:blue; font-weight:bold;">Use it here</a></p>
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* **Developed by:** `Recompense` Me!
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* **Model type:** Image Classification (Transfer Learning with a CNN backbone)
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