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language: en
license: apache-2.0
tags:
- image-classification
- green-ai
- energy-efficiency
- computer-vision
- efficientnetv2
- eden-framework
- e2am
- sustainable-ai
datasets:
- imagenet
metrics:
- accuracy
co2_eq_emissions:
emissions: 1.3947
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-EfficientNetV2-Custom-ImageNet300
results:
- task:
type: image-classification
name: Image Classification
dataset:
name: Custom-ImageNet300
type: imagenet
metrics:
- type: accuracy
value: 0.9895
name: Accuracy
- type: f1
value: 0.9891
name: F1 Score
---
# EDEN-EfficientNetV2-Custom-ImageNet300 — *SOTA Optimized*
> **Primary KPI:** EAG (Energy-to-Accuracy Gradient) = `-8.6906e-11` 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 – Progressive Unfreezing + AMP (E2AM SOTA)
## 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 | **EfficientNetV2 (EDEN)** | Δ |
|---|---|---|---|
| Accuracy | 0.9573 | **0.9895** | `+3.21%` |
| Total Energy (J) | 380,392,115 | **10,570,275** | `97.22% saved` |
| CO₂ Emissions (kg) | 50.1906 | **1.3947** | — |
| **EAG Score** | — | **-8.6906e-11** | 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 pre-loaded into pinned System RAM to eliminate disk I/O power spikes.
**Phase 2 – Progressive Unfreezing:** Backbone frozen for the first `E_unfreeze` epochs (only the classification head trains). At `E_unfreeze`, all layers are unfrozen and the learning rate is decayed. Gradient accumulation over N micro-batches simulates large batch sizes without proportional VRAM cost, slashing power-draw spikes.
**AMP (Automated Mixed Precision):** `torch.cuda.amp.autocast()` halves GPU memory bandwidth, reducing energy per backward pass.
**Sparse Regularisation:** L1 penalty `λ·Σ|W|` applied to trainable weights, driving dead neurons to zero and enabling future pruning.
## Training Statistics
| Metric | Value |
|---|---|
| Final Accuracy | 0.9895 (98.95%) |
| Total Energy Consumed | 10,570,275 J (2.9362 kWh) |
| Training Time | 15,538 s (4.32 hrs) |
| Estimated CO₂ | 1.3947 kg CO₂e |
| Training Log | `test1\eden_unfrozen_custom_imagenet_efficientNet.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
```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}
}
```
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