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
Update README.md
Browse files
README.md
CHANGED
|
@@ -10,7 +10,7 @@ pipeline_tag: image-classification
|
|
| 10 |
---
|
| 11 |
|
| 12 |
|
| 13 |
-
<h1 style="color:#
|
| 14 |
|
| 15 |
This model is an image classification model trained to identify different types of food from images. It was developed as part of a Food Vision project, utilizing transfer learning on a pre-trained convolutional neural network.
|
| 16 |
|
|
|
|
| 10 |
---
|
| 11 |
|
| 12 |
|
| 13 |
+
<h1 style="color:#b4464b; font-weight: bold;">🍽️ Model Card for Food Vision Model</h1>
|
| 14 |
|
| 15 |
This model is an image classification model trained to identify different types of food from images. It was developed as part of a Food Vision project, utilizing transfer learning on a pre-trained convolutional neural network.
|
| 16 |
|