EDEN-ResNet50-CIFAR-100 β€” Baseline

Primary KPI: EAG (Energy-to-Accuracy Gradient) = N/A Ξ”Acc/Ξ”Joules

Abstract

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 β€” 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 CIFAR-100 β€” 60,000 images – 100 classes (32Γ—32 px)

🟒 Green Delta Table

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

Metric ResNet50 Baseline ResNet50 (EDEN) Ξ”
Accuracy 0.9492 0.9492 N/A
Total Energy (J) 40,102,666 40,102,666 N/A saved
COβ‚‚ Emissions (kg) 5.2913 5.2913 β€”
EAG Score β€” N/A Ξ”Acc/Ξ”Joules

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 β€” 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 0.9492 (94.92%)
Total Energy Consumed 40,102,666 J (11.1396 kWh)
Training Time 2,403 s (0.67 hrs)
Estimated COβ‚‚ 5.2913 kg COβ‚‚e
Training Log test2\resnet50_CIFAR100_stats.csv

πŸ“Š Training Visualizations

Accuracy & Energy over Training

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

Training Curve

EAG Metric Trajectory

EAG = Ξ”Accuracy / Ξ”Joules β€” positive means learning more per Joule than baseline

EAG Curve

Project-Wide Overview

All EDEN models: energy vs accuracy

Collection Overview

Cite This Research

@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}
}
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Dataset used to train Shanmuk4622/EDEN-ResNet50-CIFAR-100

Evaluation results