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language: en
license: apache-2.0
tags:
  - image-classification
  - green-ai
  - energy-efficiency
  - computer-vision
  - unet
  - eden-framework
  - e2am
  - sustainable-ai
datasets:
  - cifar10
metrics:
  - accuracy
co2_eq_emissions:
  emissions: 0
  unit: kg
  source: Estimated via CodeCarbon (grid factor 0.475 kg CO2e/kWh)
  hardware_used: NVIDIA GeForce GTX 1080 Ti
dataset_info:
  dataset_size: 60,000 images – 10 classes (32×32 px)
model-index:
  - name: EDEN-UNet-CIFAR-10
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: CIFAR-10
          type: cifar10
        metrics:
          - type: accuracy
            value: 0.9951
            name: Accuracy

EDEN-UNet-CIFAR-10 — EDEN Classic

Primary KPI: EAG (Energy-to-Accuracy Gradient) = N/A ΔAcc/ΔJoules

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 — maximising predictive power per Joule consumed.

Applied Technique: Phase 2 – EDEN Classic Energy-Aware Sparse Training

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-10 — 60,000 images – 10 classes (32×32 px)

🟢 Green Delta Table

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

Metric	ResNet50 Baseline	UNet (EDEN)	Δ
Accuracy	0.9354	0.9951	`N/A`
Total Energy (J)	40,291,739	0	`N/A saved`
CO₂ Emissions (kg)	5.3163	0.0000	—
EAG Score	—	N/A	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

Phase 1 – Zero-Overhead Initialization: Dataset cached in System RAM.

Phase 2 – EDEN Classic: Energy-aware training loop on classic CNN architectures. Applies the same EAG early-exit criterion (EAG < γ_EAG for 3 consecutive epochs → terminate), L1 sparsity penalty, and AMP to architectures like ResNet, VGG, AlexNet, DenseNet, InceptionV3, and UNet.

Training Statistics

Metric	Value
Final Accuracy	0.9951 (99.51%)
Total Energy Consumed	0 J (0.0000 kWh)
Training Time	0 s (0.00 hrs)
Estimated CO₂	0.0000 kg CO₂e
Training Log	`test3\unet_classifier_EDEN_CIFAR10_stats.csv`

📊 Training Visualizations

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

@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}
}