Keras
TF-Keras
computer-vision
image-regression
nutrition5k
deit
tensorflow
multimodal
rgb-depth
raw-depth
Instructions to use AIJonas/model-6-deit-rgb-plus-raw-depth-carb-regression with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use AIJonas/model-6-deit-rgb-plus-raw-depth-carb-regression 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/model-6-deit-rgb-plus-raw-depth-carb-regression") - Notebooks
- Google Colab
- Kaggle
Model 6: DeiT RGB + raw depth carbohydrate regression
This model predicts total carbohydrate from overhead RGB images and overhead raw depth images.
Architecture
- RGB backbone:
deit_base_distilled_patch16_384_imagenet - Input size:
384x384 - RGB branch uses pretrained DeiT
- Raw depth branch uses a lightweight CNN
- Depth branch also creates a soft guidance mask for the RGB input
- Fusion head combines RGB and depth features
- 2-stage fine-tuning
Test results
Stage 1
- Loss: 89.8303
- MAE: 6.3148
- MSE: 89.8303
Stage 2
- Loss: 63.8722
- MAE: 5.2992
- MSE: 63.8722
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