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This model is available strictly for non-commercial academic, research, and personal educational purposes. Any commercial use, redistribution, SaaS/mobile app integration, or serving predictions via commercial APIs requires an official commercial license from the author.

🧬 DINOv3 ViT-L/16 — Whole Body Fat Percentage (WBFP) Estimator

Clinical DEXA Correlation and Bland-Altman Agreement

📌 Executive Summary

This repository contains the production-grade Vision Transformer model (39C / 39CC) developed as part of a Master's Thesis (TFM) at Universitat Politècnica de València (UPV) for the Master's Degree in Artificial Intelligence, Pattern Recognition, and Digital Imaging (MIARFID).

The model automatically estimates Whole Body Fat Percentage (WBFP) from a single frontal RGB torso photograph completely offline on-device (Edge AI), achieving clinical agreement with hospital Dual-Energy X-ray Absorptiometry (DEXA) gold standards without sending biometric imagery to cloud servers (100% GDPR / HIPAA compliant by design).

Author: Sergi Cortés Guerrero (Universitat Politècnica de València)
Academic Tutor: Dr. Juan Jesús Izquierdo Doménech (DSIC / ETSINF)
Gold Standard Reference: Hospital DEXA Whole Body Scanners
Test Cohort (Frozen Hold-Out): $N = 23$ patients with clinical DEXA ground truth

🔬 Clinical Evidence & Validation (DEXA Ground Truth)

The model was rigorously validated on a completely isolated, frozen clinical hold-out test set ($N=23$) with ground-truth DEXA measurements:

1. Correlation & Bland-Altman Agreement vs DEXA

As shown in the clinical validation plot above:

Pearson Correlation Coefficient ($r$): $0.92$ ($p < 0.001$, $95%\text{ CI } [0.80, 0.98]$)
Standard Error of Estimate (SEE / RMSE): $2.96%$ (with 10x Test-Time Augmentation) / $2.98%$ (standard single-pass)
Mean Absolute Error (MAE): $2.20%$ (with 10x TTA) / $2.28%$ (standard single-pass)
Bland-Altman 95% Limits of Agreement: Zero systematic proportional bias across the entire physiological spectrum (from lean athletic individuals at $6%$ to clinical obesity at $>38%$).

DEXA Correlation

2. Research Progression & Architectural Breakthroughs

Across 119 controlled experiments, Vision Transformers fine-tuned with Low-Rank Adaptation (LoRA) and optimized via Knowledge Distillation significantly outperformed traditional ConvNets, 3D parametric morphing (SMPL), and Multimodal Large Language Models (VLLMs):

Error Reduction across R&D Phases

Architecture / Paradigm	Description	Test SEE (%)	MAE (%)	Pearson $r$	Clinical Assessment
SMPL 3D Mesh + SVR	3D Body Mesh Morphing	7.81%	6.20%	0.58	Insufficient surface texture details
ConvNeXt-Base	Deep Convolutional Baseline	6.84%	5.21%	0.72	Severe overfitting on small cohorts
Gemini 3.1 Flash (VLLM)	Zero-shot visual prompt	4.92%	3.85%	0.82	High variance at physiological extremes
DINOv3 ViT-L/16 Base	Direct Scalar MSE Regression	4.20%	3.30%	0.84	Baseline self-supervised ViT
Model 35-A (Distributional)	41-Bin Wasserstein Expected Loss	3.75%	2.85%	0.88	Captures epistemic visual uncertainty
Model 37-DA (Silver Curated)	Source-Differentiated LoRA	3.23%	2.45%	0.90	High precision on high-adiposity cohorts
Model 39-C (Production Single)	Teacher-Distilled Monomodel	2.98%	2.28%	0.92	Production Single Model (This Repo)
Model 39-CC (Single + TTA 10x)	Teacher-Distilled + 10x TTA	2.96%	2.20%	0.92	Best On-Device Single Model SOTA
Ensemble HSR-3Way (Oracle)	3-Way Specialist Routing	2.61%	1.98%	0.95	Global Benchmark Record (Enterprise Tier)

Complete Leaderboard

📱 Edge AI & Mobile Deployment (100% On-Device)

This model was specifically designed for real-time on-device inference in mobile applications without requiring internet access or cloud GPUs:

Mobile App Camera View Mobile App Prediction and Feedback

On-Device Hardware Benchmark (Snapdragon 8 Gen 2 / Apple A16 Bionic)

Weight Variant	Format	File Size	Size Reduction	Mobile CPU Latency	Accuracy Loss
`final_epoch_model.pth`	PyTorch FP32	1.21 GB	Baseline	~580 ms	0.00% (Baseline)
`final_epoch_model_flat.onnx`	ONNX FP32	1.16 GB	-4.1%	~540 ms	0.00% (Lossless)
`final_epoch_model_fp16.onnx`	ONNX FP16	581 MB	-51.5%	~310 ms	< 0.01% (Undetectable)
`final_epoch_model_selective_int8.onnx`	ONNX INT8	294 MB	-75.6%	~210 ms	< 0.05% (Clinical Grade)

📦 Available Files & Checkpoints

All model variants are accessible upon approved gated access:

final_epoch_model_flat.onnx (1.16 GB): Self-contained single-file FP32 ONNX model compatible with onnxruntime on Android, iOS, Windows, Linux, and macOS.
final_epoch_model_fp16.onnx (581 MB): Half-precision FP16 ONNX model optimized for mobile GPUs and NPUs.
final_epoch_model_selective_int8.onnx (294 MB): Dynamic INT8 quantized ViT backbone with FP32 head, designed for low-memory mobile CPUs.
final_epoch_model.pth (1.21 GB): Native PyTorch FP32 weights (DINOv3 ViT-L/16 backbone + LoRA adapters + distributional head).

💻 Python Inference Example

import numpy as np
import onnxruntime as ort
from PIL import Image
from huggingface_hub import hf_hub_download

# 1. Download model from gated HF repository (requires approved access token)
model_path = hf_hub_download(
    repo_id="Sariax/dinov3-body-fat-onnx",
    filename="final_epoch_model_flat.onnx",
    token="YOUR_HF_TOKEN"
)

# 2. Initialize ONNX Session (CPU or CUDA)
session = ort.InferenceSession(model_path)

# Bin centers from 5.0% to 45.0% (41 continuous bins)
bin_centers = np.linspace(5.0, 45.0, 41)

# 3. Preprocess Image (224x224, ImageNet Normalized NCHW)
def preprocess(image_path):
    img = Image.open(image_path).convert('RGB').resize((224, 224))
    arr = np.array(img).astype(np.float32) / 255.0
    mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)
    std = np.array([0.229, 0.224, 0.225], dtype=np.float32)
    norm = (arr - mean) / std
    tensor = np.transpose(norm, (2, 0, 1)) # HWC -> CHW
    return np.expand_dims(tensor, axis=0)  # Add batch dim -> [1, 3, 224, 224]

# 4. Predict Softmax Distribution
input_tensor = preprocess("torso_photo.jpg")
logits = session.run(None, {"input": input_tensor})[0]
probabilities = np.exp(logits - np.max(logits)) / np.sum(np.exp(logits - np.max(logits)))

# 5. Compute Mathematical Expectation (WBFP)
estimated_wbfp = np.sum(probabilities * bin_centers)
uncertainty_std = np.sqrt(np.sum(probabilities * ((bin_centers - estimated_wbfp) ** 2)))

print(f"Estimated Body Fat: {estimated_wbfp:.2f}% (Visual Uncertainty: ±{uncertainty_std:.2f}%)")

💼 Commercial Licensing, Partnerships & Consulting

Licensing Overview

Tier	Target Audience	Permitted Use	Access
Academic & Personal	Students, Academic Researchers	Non-commercial research, thesis validation, open science	Free via Gated Access (CC BY-NC-ND 4.0)
Commercial Startup / App	Fitness Apps, Telemedicine, Smart Scales	Mobile/Web apps, SaaS products, freemium services	Commercial License Agreement
Enterprise Clinical Tier	Hospitals, Insurances, Healthtech Giants	Full Oracle Ensemble (SEE 2.61%) + Cloud / Edge deployment	Enterprise Partnership

Prohibited Without Commercial Agreement

Any commercial deployment, bundling into paid or ad-supported apps, serving predictions through commercial APIs, or redistribution of weights without a signed commercial agreement is strictly prohibited under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.

Commercial Inquiries & Custom Development

If you or your company wish to:

Acquire a commercial license for the on-device production model.
Access the Oracle Ensemble 3-Way HSR system (benchmark record: SEE 2.61%, MAE 1.98%, Pearson $r = 0.95$).
Commission custom engineering: Mobile app integration (Flutter, Swift, Kotlin), camera-guided alignment, calibration on female/athletic cohorts, or enterprise cloud deployment.

👉 How to contact:

Hugging Face: Open a formal inquiry in the Discussions tab.
Direct Contact: Contact the author Sergi Cortés Guerrero directly via Hugging Face profile or through the contact details provided upon gated access approval.

📚 Citation

If you use this model or refer to its methodology in academic work, please cite:

@mastersthesis{cortes2026bodyfat,
  title   = {Estimaci{\'o}n del porcentaje de grasa corporal mediante modelos de visi{\'o}n multimodal y despliegue en dispositivos m{\'o}viles},
  author  = {Cort{\'e}s Guerrero, Sergi},
  school  = {Universitat Polit{\`e}cnica de Val{\`e}ncia (UPV)},
  year    = {2026},
  type    = {Master's Thesis},
  address = {Valencia, Spain}
}

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