---
language: en
license: apache-2.0
tags:
- image-classification
- green-ai
- energy-efficiency
- computer-vision
- inceptionv3
- eden-framework
- reference-study
- sustainable-ai
datasets:
- cifar10
metrics:
- accuracy
---

# EDEN-InceptionV3-CIFAR-10 — *Baseline – Standard Full Training (Reference Study)*

> **Primary KPI:** EAG (Energy-to-Accuracy Gradient) — see Green Delta Table below.

## Abstract
This model is part of **Project EDEN (Energy-Driven Evolution of Networks)**.
It serves as the **Brute-Force Baseline** for the InceptionV3 architecture on CIFAR-10,
providing a transparent energy reference for EAG benchmarking against EDEN-optimized models.

**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 |
| **Dataset** | CIFAR-10 — 60,000 images – 10 classes (32×32 px) |

## 🟢 Green Delta Table
*This is the reference baseline. Compare against EDEN-optimized models for EAG.*

| Metric | InceptionV3 Baseline | EDEN Optimized | Δ |
|---|---|---|---|
| Accuracy | See CSV log | See SOTA repo | — |
| Total Energy (J) | See CSV log | See SOTA repo | — |
| **EAG Score** | — | See SOTA repo | ΔAcc/ΔJoules |

## E2AM Algorithm — Applied Phase
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.

## 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}
}
```