Keras
tensorflow
computer-vision
regression
nutrition5k
carbohydrate-estimation
multimodal
rgb-depth
efficientnetv2
Instructions to use AIJonas/nutrition5k-pretrained-efficientnetv2b0-rgb-plus-depth-carb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use AIJonas/nutrition5k-pretrained-efficientnetv2b0-rgb-plus-depth-carb with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://AIJonas/nutrition5k-pretrained-efficientnetv2b0-rgb-plus-depth-carb") - Notebooks
- Google Colab
- Kaggle
EfficientNetV2B0 RGB + CNN Depth Carbohydrate Regression
This model predicts dish-level carbohydrate content from overhead RGB images and overhead depth images from the Nutrition5K dataset.
Architecture
- Dual-input multimodal regression model
- One pretrained EfficientNetV2B0 branch for overhead RGB images
- One from-scratch CNN branch for overhead depth images
- Global average pooling on both branches
- Feature fusion through concatenation
- Fully connected regression head
- Final dense layer with linear activation for carbohydrate prediction
Backbone setup
- EfficientNetV2B0 is initialized with ImageNet pretrained weights for RGB
- The RGB backbone is frozen during the initial training stage
- The depth branch is trained from scratch
Input modalities
rgb_input: overhead RGB imagedepth_input: overhead depth image
Target
total_carb
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