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README.md

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+---
+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}
+}
+```