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@@ -21,6 +21,22 @@ co2_eq_emissions:
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  hardware_used: NVIDIA GeForce GTX 1080 Ti
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  dataset_info:
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  dataset_size: "60,000 images – 10 classes (32×32 px)"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  # EDEN-MobileViTv3-CIFAR-10 — *SOTA Optimized*
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  > **Primary KPI:** EAG (Energy-to-Accuracy Gradient) = `-1.4172e-09` ΔAcc/ΔJoules
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  ## Abstract
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- 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.
 
 
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  **Applied Technique:** Phase 2 – Progressive Unfreezing + AMP (E2AM SOTA)
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@@ -73,15 +91,25 @@ This model is part of **Project EDEN (Energy-Driven Evolution of Networks)**, im
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  | Estimated CO₂ | 1.1806 kg CO₂e |
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  | Training Log | `test1\eden_optimized_cifar10_custom_mobilevitv3.csv` |
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- ## Cite This Research
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- If you use this model, please cite the **EDEN / E2AM Framework**:
 
 
 
 
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  ```bibtex
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  @misc{eden2025,
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  title = {Project EDEN: Energy-Driven Evolution of Networks},
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  author = {EDEN Research Team},
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  year = {2025},
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- note = {Hugging Face Organization: ProjectEDEN},
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  url = {https://huggingface.co/Shanmuk4622}
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  }
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  ```
 
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  hardware_used: NVIDIA GeForce GTX 1080 Ti
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  dataset_info:
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  dataset_size: "60,000 images – 10 classes (32×32 px)"
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+ model-index:
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+ - name: EDEN-MobileViTv3-CIFAR-10
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+ results:
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+ - task:
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+ type: image-classification
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+ name: Image Classification
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+ dataset:
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+ name: CIFAR-10
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+ type: cifar10
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+ metrics:
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+ - type: accuracy
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+ value: 0.9798
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+ name: Accuracy
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+ - type: f1
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+ value: 0.9798
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+ name: F1 Score
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  ---
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  # EDEN-MobileViTv3-CIFAR-10 — *SOTA Optimized*
 
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  > **Primary KPI:** EAG (Energy-to-Accuracy Gradient) = `-1.4172e-09` ΔAcc/ΔJoules
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  ## Abstract
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+ This model is part of **Project EDEN (Energy-Driven Evolution of Networks)**, implementing the
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+ **E2AM (Energy Efficient Advanced Model)** Framework. The goal is to shift AI benchmarking from
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+ pure accuracy to *Green SOTA* — maximising predictive power per Joule consumed.
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  **Applied Technique:** Phase 2 – Progressive Unfreezing + AMP (E2AM SOTA)
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  | Estimated CO₂ | 1.1806 kg CO₂e |
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  | Training Log | `test1\eden_optimized_cifar10_custom_mobilevitv3.csv` |
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+ ## 📊 Training Visualizations
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+
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+ ### Accuracy & Energy over Training
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+ > Green = accuracy (left axis) · Orange dashed = cumulative energy (right axis)
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+
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+ ![Training Curve](training_curve.png)
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+ ### Project-Wide Overview
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+ *All EDEN models: energy vs accuracy*
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+
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+ ![Collection Overview](https://huggingface.co/Shanmuk4622/EDEN-Core-Scripts/resolve/main/energy_accuracy_overview.png)
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+
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+ ## Cite This Research
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  ```bibtex
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  @misc{eden2025,
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  title = {Project EDEN: Energy-Driven Evolution of Networks},
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  author = {EDEN Research Team},
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  year = {2025},
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+ note = {Hugging Face: Shanmuk4622},
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  url = {https://huggingface.co/Shanmuk4622}
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  }
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  ```