---
language: en
license: apache-2.0
tags:
- image-classification
- green-ai
- energy-efficiency
- computer-vision
- mobilevitv3
- eden-framework
- e2am
- sustainable-ai
datasets:
- cifar10
metrics:
- accuracy
co2_eq_emissions:
  emissions: 1.1806
  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 – 10 classes (32×32 px)"
model-index:
- name: EDEN-MobileViTv3-CIFAR-10
  results:
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: CIFAR-10
      type: cifar10
    metrics:
    - type: accuracy
      value: 0.9798
      name: Accuracy
    - type: f1
      value: 0.9798
      name: F1 Score
---

# EDEN-MobileViTv3-CIFAR-10 — *SOTA Optimized*

> **Primary KPI:** EAG (Energy-to-Accuracy Gradient) = `-1.4172e-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* — 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** | CIFAR-10 — 60,000 images – 10 classes (32×32 px) |

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

| Metric | ResNet50 Baseline | **MobileViTv3 (EDEN)** | Δ |
|---|---|---|---|
| Accuracy | 0.9354 | **0.9798** | `+4.44%` |
| Total Energy (J) | 40,291,739 | **8,947,585** | `77.79% saved` |
| CO₂ Emissions (kg) | 5.3163 | **1.1806** | — |
| **EAG Score** | — | **-1.4172e-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

**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 | 0.9798 (97.98%) |
| Total Energy Consumed | 8,947,585 J (2.4854 kWh) |
| Training Time | 13,470 s (3.74 hrs) |
| Estimated CO₂ | 1.1806 kg CO₂e |
| Training Log | `test1\eden_optimized_cifar10_custom_mobilevitv3.csv` |

## 📊 Training Visualizations

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

![Training Curve](training_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}
}
```