---
language: en
license: apache-2.0
tags:
- image-classification
- green-ai
- energy-efficiency
- computer-vision
- resnet50
- eden-framework
- e2am
- sustainable-ai
datasets:
- cifar100
metrics:
- accuracy
co2_eq_emissions:
  emissions: 5.2913
  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)"
model-index:
- name: EDEN-ResNet50-CIFAR-100
  results:
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: CIFAR-100
      type: cifar100
    metrics:
    - type: accuracy
      value: 0.9492
      name: Accuracy
    - type: f1
      value: 0.9492
      name: F1 Score
---

# EDEN-ResNet50-CIFAR-100 — *Baseline*

> **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:** 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 | **ResNet50 (EDEN)** | Δ |
|---|---|---|---|
| Accuracy | 0.9492 | **0.9492** | `N/A` |
| Total Energy (J) | 40,102,666 | **40,102,666** | `N/A saved` |
| CO₂ Emissions (kg) | 5.2913 | **5.2913** | — |
| **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

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 CO₂ | 5.2913 kg CO₂e |
| Training Log | `test2\resnet50_CIFAR100_stats.csv` |

## 📊 Training Visualizations

### Accuracy & Energy over Training
> Green = accuracy (left axis)  ·  Orange dashed = cumulative energy (right axis)

![Training Curve](training_curve.png)

### EAG Metric Trajectory
> EAG = ΔAccuracy / ΔJoules — positive means learning more per Joule than baseline

![EAG Curve](eag_curve.png)

### Project-Wide Overview
*All EDEN models: energy vs accuracy*

![Collection Overview](https://huggingface.co/Shanmuk4622/EDEN-Core-Scripts/resolve/main/energy_accuracy_overview.png)

## 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}
}
```