This model is part of Project EDEN (Energy-Driven Evolution of Networks), implementing the E2AM (Energy Efficient Advanced Model) Framework. The goal is to shift AI benchmarking from pure accuracy to Green SOTA — maximising predictive power per Joule consumed.

Applied Technique: Baseline – Standard Full Training (Reference Study)

Profiling Environment

Component	Specification
GPU	NVIDIA GeForce GTX 1080 Ti (11 GB VRAM, 250 W TDP)
CPU	Intel Xeon W-2125 (4 cores / 8 threads @ 4.00 GHz)
RAM	63.66 GB System RAM
OS	Windows 10
Dataset	Custom-ImageNet300 — ~450,000 images – 300 classes (224 px)

🟢 Green Delta Table

Comparing this model against the reference baseline (ResNet-50 equivalent)

Metric	ResNet50 Baseline	VGG16 (EDEN)	Δ
Accuracy	0.9573	0.9951	`+3.78%`
Total Energy (J)	380,392,115	20,389,217	`94.64% saved`
CO₂ Emissions (kg)	50.1906	2.6902	—
EAG Score	—	-1.0507e-10	ΔAcc/ΔJoules

A positive EAG means this model learns more per Joule than the baseline. A negative EAG indicates a trade-off where higher accuracy required more energy investment.

E2AM Algorithm — Applied Phases

Standard full fine-tuning used as the Brute-Force Baseline for energy comparison. All layers trained from epoch 1 with a fixed learning rate and no gradient accumulation. Included for transparent EAG benchmarking.

Training Statistics

Metric	Value
Final Accuracy	0.9951 (99.51%)
Total Energy Consumed	20,389,217 J (5.6637 kWh)
Training Time	28,726 s (7.98 hrs)
Estimated CO₂	2.6902 kg CO₂e
Training Log	`test2\vgg16_CustomImageNet300_stats.csv`

📊 Training Visualizations

Accuracy & Energy over Training

Green = accuracy (left axis) · Orange dashed = cumulative energy (right axis)

EAG Metric Trajectory

EAG = ΔAccuracy / ΔJoules — positive means learning more per Joule than baseline

Project-Wide Overview

All EDEN models: energy vs accuracy

Cite This Research

bibtex

@misc{eden2025,
  title     = {Project EDEN: Energy-Driven Evolution of Networks},
  author    = {EDEN Research Team},
  year      = {2025},
  note      = {Hugging Face: Shanmuk4622},
  url       = {https://huggingface.co/Shanmuk4622}
}

Shanmuk4622/EDEN-VGG16-Custom-ImageNet300

EDEN-VGG16-Custom-ImageNet300 — Baseline

Abstract