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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
+- efficientnetv2
+- eden-framework
+- e2am
+- sustainable-ai
+datasets:
+- cifar100
+metrics:
+- accuracy
+co2_eq_emissions:
+  emissions: 1.9489
+  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 – 100 classes (32×32 px)"
+---
+
+# EDEN-EfficientNetV2-CIFAR-100 — *SOTA Optimized*
+
+> **Primary KPI:** EAG (Energy-to-Accuracy Gradient) = `-1.6161e-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* — maximizing 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-100 — 60,000 images – 100 classes (32×32 px) |
+
+## 🟢 Green Delta Table
+*Comparing this model against the reference baseline (ResNet-50 equivalent)*
+
+| Metric | ResNet50 Baseline | **EfficientNetV2 (EDEN)** | Δ |
+|---|---|---|---|
+| Accuracy | 0.9492 | **0.9902** | `+4.09%` |
+| Total Energy (J) | 40,102,666 | **14,770,619** | `63.17% saved` |
+| CO₂ Emissions (kg) | 5.2913 | **1.9489** | — |
+| **EAG Score** | — | **-1.6161e-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 | 0.9902 (99.02%) |
+| Total Energy Consumed | 14,770,619 J (4.1029 kWh) |
+| Training Time | 21,737 s (6.04 hrs) |
+| Estimated CO₂ | 1.9489 kg CO₂e |
+| Training Log | `test1\eden_unfrozen_cifar100_efficientNet.csv` |
+
+## Cite This Research
+If you use this model, please cite the **EDEN / E2AM Framework**:
+
+```bibtex
+@misc{eden2025,
+  title     = {Project EDEN: Energy-Driven Evolution of Networks},
+  author    = {EDEN Research Team},
+  year      = {2025},
+  note      = {Hugging Face Organization: ProjectEDEN},
+  url       = {https://huggingface.co/Shanmuk4622}
+}
+```