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README.md

@@ -21,14 +21,32 @@ co2_eq_emissions:
   hardware_used: NVIDIA GeForce GTX 1080 Ti
 dataset_info:
   dataset_size: "60,000 images – 100 classes (32×32 px)"
+model-index:
+- name: EDEN-ResNet50-CIFAR-100
+  results:
+  - task:
+      type: image-classification
+      name: Image Classification
+    dataset:
+      name: CIFAR-100
+      type: cifar100
+    metrics:
+    - type: accuracy
+      value: 0.9492
+      name: Accuracy
+    - type: f1
+      value: 0.9492
+      name: F1 Score
 ---
 
 # EDEN-ResNet50-CIFAR-100 — *Baseline*
 
-> **Primary KPI:** EAG (Energy-to-Accuracy Gradient) = `nan` ΔAcc/ΔJoules
+> **Primary KPI:** EAG (Energy-to-Accuracy Gradient) = `N/A` 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.
+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:** Baseline – Standard Full Training (Reference Study)
 
@@ -46,10 +64,10 @@ This model is part of **Project EDEN (Energy-Driven Evolution of Networks)**, im
 
 | Metric | ResNet50 Baseline | **ResNet50 (EDEN)** | Δ |
 |---|---|---|---|
-| Accuracy | 0.9492 | **0.9492** | `+0.00%` |
-| Total Energy (J) | 40,102,666 | **40,102,666** | `0.00% saved` |
+| Accuracy | 0.9492 | **0.9492** | `N/A` |
+| Total Energy (J) | 40,102,666 | **40,102,666** | `N/A saved` |
 | CO₂ Emissions (kg) | 5.2913 | **5.2913** | — |
-| **EAG Score** | — | **nan** | ΔAcc/ΔJoules |
+| **EAG Score** | — | **N/A** | 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.
@@ -67,15 +85,30 @@ Standard full fine-tuning used as the **Brute-Force Baseline** for energy compar
 | Estimated CO₂ | 5.2913 kg CO₂e |
 | Training Log | `test2\resnet50_CIFAR100_stats.csv` |
 
-## Cite This Research
-If you use this model, please cite the **EDEN / E2AM Framework**:
+## 📊 Training Visualizations
+
+### Accuracy & Energy over Training
+> Green = accuracy (left axis)  ·  Orange dashed = cumulative energy (right axis)
+
+![Training Curve](training_curve.png)
+
+### EAG Metric Trajectory
+> EAG = ΔAccuracy / ΔJoules — positive means learning more per Joule than baseline
 
+![EAG Curve](eag_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 Organization: ProjectEDEN},
+  note      = {Hugging Face: Shanmuk4622},
   url       = {https://huggingface.co/Shanmuk4622}
 }
 ```