---
language: en
license: apache-2.0
tags:
- image-classification
- green-ai
- energy-efficiency
- computer-vision
- resnet50
- eden-framework
- e2am
- sustainable-ai
datasets:
- imagenet
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: "~450,000 images – 300 classes (224 px)"
model-index:
- name: EDEN-ResNet50-Custom-ImageNet300
  results:
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: Custom-ImageNet300
      type: imagenet
    metrics:
    - type: accuracy
      value: 0.9999
      name: Accuracy
---

# EDEN-ResNet50-Custom-ImageNet300 — *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** | Custom-ImageNet300 — ~450,000 images – 300 classes (224 px) |

## 🟢 Green Delta Table
*Comparing this model against the reference baseline (ResNet-50 equivalent)*

| Metric | ResNet50 Baseline | **ResNet50 (EDEN)** | Δ |
|---|---|---|---|
| Accuracy | 0.9573 | **0.9999** | `N/A` |
| Total Energy (J) | 380,392,115 | **0** | `N/A saved` |
| CO₂ Emissions (kg) | 50.1906 | **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 | 0.9999 (99.99%) |
| Total Energy Consumed | 0 J (0.0000 kWh) |
| Training Time | 0 s (0.00 hrs) |
| Estimated CO₂ | 0.0000 kg CO₂e |
| Training Log | `test3\resnet50_EDEN_CustomImageNet300_stats.csv` |

## 📊 Training Visualizations

### 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}
}
```