| ---
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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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| - resnet50
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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
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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)"
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| model-index:
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| - name: EDEN-ResNet50-Custom-ImageNet300
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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: Custom-ImageNet300
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| type: imagenet
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| metrics:
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| - type: accuracy
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| value: 0.9999
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| name: Accuracy
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| ---
|
|
|
| # EDEN-ResNet50-Custom-ImageNet300 — *EDEN Classic*
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|
|
| > **Primary KPI:** EAG (Energy-to-Accuracy Gradient) = `N/A` ΔAcc/ΔJoules
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|
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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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|
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| **Applied Technique:** Phase 2 – EDEN Classic Energy-Aware Sparse Training
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|
|
| ## Profiling Environment
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| | Component | Specification |
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| |---|---|
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| | **GPU** | NVIDIA GeForce GTX 1080 Ti (11 GB VRAM, 250 W TDP) |
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| | **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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|
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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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|
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| | Metric | ResNet50 Baseline | **ResNet50 (EDEN)** | Δ |
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| |---|---|---|---|
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| | Accuracy | 0.9573 | **0.9999** | `N/A` |
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| | Total Energy (J) | 380,392,115 | **0** | `N/A saved` |
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| | CO₂ Emissions (kg) | 50.1906 | **0.0000** | — |
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| | **EAG Score** | — | **N/A** | ΔAcc/ΔJoules |
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|
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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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|
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| ## E2AM Algorithm — Applied Phases
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|
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| **Phase 1 – Zero-Overhead Initialization:** Dataset cached in System RAM.
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|
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| **Phase 2 – EDEN Classic:** Energy-aware training loop on classic CNN architectures. Applies the same EAG early-exit criterion (`EAG < γ_EAG` for 3 consecutive epochs → terminate), L1 sparsity penalty, and AMP to architectures like ResNet, VGG, AlexNet, DenseNet, InceptionV3, and UNet.
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|
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| ## Training Statistics
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| | Metric | Value |
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| |---|---|
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| | Final Accuracy | 0.9999 (99.99%) |
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| | Total Energy Consumed | 0 J (0.0000 kWh) |
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| | Training Time | 0 s (0.00 hrs) |
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| | Estimated CO₂ | 0.0000 kg CO₂e |
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| | Training Log | `test3\resnet50_EDEN_CustomImageNet300_stats.csv` |
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|
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| ## 📊 Training Visualizations
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|
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| ### EAG Metric Trajectory
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| > EAG = ΔAccuracy / ΔJoules — positive means learning more per Joule than baseline
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|
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| 
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|
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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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| 
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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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| ```
|
|
|
