Model card: drop calibration warning

README.md

@@ -18,17 +18,12 @@ language:
 # DPF-Nutrition (RGB-D) — weights & Core ML
 
 Trained weights for **DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion**
-(Han et al., *Foods* 2023, [arXiv:2310.11702](https://arxiv.org/abs/2310.11702)), reproduced under the
-**official Nutrition5k train/test splits** (after fixing an 82% hash-split data leak).
+(Han et al., *Foods* 2023, [arXiv:2310.11702](https://arxiv.org/abs/2310.11702)), trained and evaluated
+on **Nutrition5k** under the official train/test splits, and converted to Core ML for on-device use.
 
 - 🔬 Research repo (training & reproduction): https://github.com/T0MYYY/Nutrition5k
 - 📱 iOS deployment app (Core ML): https://github.com/T0MYYY/CalBro
-
-## ⚠️ Important — not calibrated for real use
-
-These weights are a **research/student reproduction**. The backbone is **not** rigorously calibrated for
-phone capture or real plated-food ground truth. **Outputs have no reference value** and must **not** be used
-for medical, dietary, or clinical decisions.
+- 🤗 Reproduction & ablation checkpoints: https://huggingface.co/T0MYYY/nutrition5k-experiments
 
 ## Contents
 
@@ -37,22 +32,21 @@ for medical, dietary, or clinical decisions.
 | `dpf_nutrition_food2k.pt` | DPF-Nutrition PyTorch checkpoint (Food2K ResNet-101 backbone, RGB-D fusion head) |
 | `food2k_resnet101.pth` | Food2K ResNet-101 pretrained backbone used by DPF |
 | `coreml/DPFNutritionRGBDepth.mlpackage` | DPF-Nutrition converted to Core ML (rgb+depth → [cal, mass, fat, carb, protein]) |
-| `coreml/DepthAnythingV2SmallF16P6.mlpackage` | Depth Anything V2 (Small) Core ML, 518×392, used as the depth stage in CalBro |
+| `coreml/DepthAnythingV2SmallF16P6.mlpackage` | Depth Anything V2 (Small) Core ML, 518×392, depth stage used in CalBro |
 
 ## Pipeline
 
 `RGB → Depth Anything V2 (predicted depth) → DPF-Nutrition RGB-D fusion → [calories, mass, fat, carbs, protein]`
 
-On LiDAR iPhones the hardware depth can replace the monocular depth stage (future work).
+On LiDAR iPhones the hardware depth can replace the monocular depth stage.
 
 ## Citation
 
 ```bibtex
 @article{han2023dpfnutrition,
-  title   = {DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion},
-  author  = {Han, Yuzhe and Cheng, Qimin and Wu, Wenjin and Huang, Ziyang},
-  journal = {Foods}, volume = {12}, number = {23}, pages = {4293}, year = {2023},
-  doi = {10.3390/foods12234293}
+  title = {DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion},
+  author = {Han, Yuzhe and Cheng, Qimin and Wu, Wenjin and Huang, Ziyang},
+  journal = {Foods}, volume = {12}, number = {23}, pages = {4293}, year = {2023}, doi = {10.3390/foods12234293}
 }
 @inproceedings{thames2021nutrition5k,
   title = {Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food},