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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:
  - cifar100
metrics:
  - accuracy
co2_eq_emissions:
  emissions: 2.2711
  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 – 100 classes (32×32 px)

EDEN-UNet-CIFAR-100 — Baseline

Primary KPI: EAG (Energy-to-Accuracy Gradient) = -2.0585e-09 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 — maximizing 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 — 60,000 images – 100 classes (32×32 px)

🟢 Green Delta Table

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

Metric	ResNet50 Baseline	UNet (EDEN)	Δ
Accuracy	0.9492	0.9963	`+4.71%`
Total Energy (J)	40,102,666	17,212,640	`57.08% saved`
CO₂ Emissions (kg)	5.2913	2.2711	—
EAG Score	—	-2.0585e-09	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	0.9963 (99.63%)
Total Energy Consumed	17,212,640 J (4.7813 kWh)
Training Time	1,052 s (0.29 hrs)
Estimated CO₂	2.2711 kg CO₂e
Training Log	`test2\unet_CIFAR100_stats.csv`

Cite This Research

If you use this model, please cite the EDEN / E2AM Framework:

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