---
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**:

```bibtex
@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}
}
```