---
license: cc-by-4.0
tags:
- food
- nutrition
- calorie-estimation
- regression
- pytorch
---

# NutriConsistNet (V2)

Predicts total calories, mass, fat, carb and protein of a plate from ONE overhead RGB photo.

- Backbone: ResNet-50 (ImageNet pretrained)
- Structure: mass x per-gram density decomposition (totals = density * mass)
- Trained with an Atwater energy-consistency loss:
  calories = 4*protein + 4*carb + 9*fat (soft penalty, lambda = 0.1)
- V2 additionally supervises the density head and anchors mass more strongly
- Dataset: Nutrition5k, overhead RGB subset, official train/test split
- Input: one 320x320 RGB overhead photo, ImageNet normalization (no depth needed)
- Output order: [calories(kcal), mass(g), fat(g), carb(g), protein(g)]

## Test results (official Nutrition5k test split, single-frame protocol)
Calories MAE: 44.7 kcal (17.5%)
Mass MAE: 26.2 g (13.2%)
Fat MAE: 3.4 g (26.5%)
Carb MAE: 5.0 g (25.2%)
Protein MAE: 4.3 g (24.6%)
Atwater violation of final predictions: 8.3 kcal

## How to load
```python
import numpy as np, torch, torch.nn as nn, torchvision.models as tvm
# paste the model class from the training notebook, then:
means = np.load("train_means.npy")
model = NutriConsistNetV2(means)      # for the v2 file
# model = NutriConsistNet()           # for the e2 file
model.load_state_dict(torch.load("nutriconsistnet_v2.pt", map_location="cpu"))
model.eval()
```