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language: en
license: apache-2.0
tags:
  - image-classification
  - green-ai
  - energy-efficiency
  - computer-vision
  - inceptionv3
  - eden-framework
  - e2am
  - sustainable-ai
datasets:
  - imagenet
metrics:
  - accuracy
co2_eq_emissions:
  emissions: 77.4491
  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-InceptionV3-Custom-ImageNet300
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: Custom-ImageNet300
          type: imagenet
        metrics:
          - type: accuracy
            value: 0.9994
            name: Accuracy
          - type: f1
            value: 0.9994
            name: F1 Score

EDEN-InceptionV3-Custom-ImageNet300 — Baseline

Primary KPI: EAG (Energy-to-Accuracy Gradient) = 2.0390e-10 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	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	InceptionV3 (EDEN)	Δ
Accuracy	0.9573	0.9994	`+4.21%`
Total Energy (J)	380,392,115	586,982,974	`-54.31% saved`
CO₂ Emissions (kg)	50.1906	77.4491	—
EAG Score	—	2.0390e-10	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	0.9994 (99.94%)
Total Energy Consumed	586,982,974 J (163.0508 kWh)
Training Time	32,248 s (8.96 hrs)
Estimated CO₂	77.4491 kg CO₂e
Training Log	`test2\inceptionv3_CustomImageNet300_stats.csv`

📊 Training Visualizations

Accuracy & Energy over Training

Green = accuracy (left axis) · Orange dashed = cumulative energy (right axis)

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}
}