| ---
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| language: en
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| license: apache-2.0
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| tags:
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| - image-classification
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| - green-ai
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| - energy-efficiency
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| - computer-vision
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| - mobilevitv3
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| - eden-framework
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| - e2am
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| - sustainable-ai
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| datasets:
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| - imagenet
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| metrics:
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| - accuracy
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| co2_eq_emissions:
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| emissions: 0.9111
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| unit: kg
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| source: Estimated via CodeCarbon (grid factor 0.475 kg CO2e/kWh)
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| hardware_used: NVIDIA GeForce GTX 1080 Ti
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| dataset_info:
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| dataset_size: "~450,000 images – 300 classes (224 px)"
|
| ---
|
|
|
| # EDEN-MobileViTv3-Custom-ImageNet300 — *SOTA Optimized*
|
|
|
| > **Primary KPI:** EAG (Energy-to-Accuracy Gradient) = `1.9376e-10` ΔAcc/ΔJoules
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|
|
| ## 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.
|
|
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| **Applied Technique:** Phase 2 – Progressive Unfreezing + AMP (E2AM SOTA)
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|
|
| ## Profiling Environment
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| | Component | Specification |
|
| |---|---|
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| | **GPU** | NVIDIA GeForce GTX 1080 Ti (11 GB VRAM, 250 W TDP) |
|
| | **CPU** | Intel Xeon W-2125 (4 cores / 8 threads @ 4.00 GHz) |
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| | **RAM** | 63.66 GB System RAM |
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| | **OS** | Windows 10 |
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| | **Dataset** | Custom-ImageNet300 — ~450,000 images – 300 classes (224 px) |
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|
|
| ## 🟢 Green Delta Table
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| *Comparing this model against the reference baseline (ResNet-50 equivalent)*
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|
|
| | Metric | ResNet50 Baseline | **MobileViTv3 (EDEN)** | Δ |
|
| |---|---|---|---|
|
| | Accuracy | 0.9573 | **0.8850** | `-7.24%` |
|
| | Total Energy (J) | 380,392,115 | **6,905,436** | `98.18% saved` |
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| | CO₂ Emissions (kg) | 50.1906 | **0.9111** | — |
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| | **EAG Score** | — | **1.9376e-10** | ΔAcc/ΔJoules |
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|
|
| > A **positive EAG** means this model learns more per Joule than the baseline.
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| > A **negative EAG** indicates a trade-off where higher accuracy required more energy investment.
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|
|
| ## E2AM Algorithm — Applied Phases
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|
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| **Phase 1 – Zero-Overhead Initialization:** Dataset pre-loaded into pinned System RAM to eliminate disk I/O power spikes.
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|
|
| **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.
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|
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| **AMP (Automated Mixed Precision):** `torch.cuda.amp.autocast()` halves GPU memory bandwidth, reducing energy per backward pass.
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|
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| **Sparse Regularisation:** L1 penalty `λ·Σ|W|` applied to trainable weights, driving dead neurons to zero and enabling future pruning.
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|
|
| ## Training Statistics
|
| | Metric | Value |
|
| |---|---|
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| | Final Accuracy | 0.8850 (88.50%) |
|
| | Total Energy Consumed | 6,905,436 J (1.9182 kWh) |
|
| | Training Time | 10,333 s (2.87 hrs) |
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| | Estimated CO₂ | 0.9111 kg CO₂e |
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| | Training Log | `test1\eden_optimized_custom_imagenet_mobilevitv3.csv` |
|
|
|
| ## Cite This Research
|
| 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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| }
|
| ```
|
|
|
