| ---
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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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| - inceptionv3
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| - eden-framework
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| - reference-study
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| - sustainable-ai
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| datasets:
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| - cifar10
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| metrics:
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| - accuracy
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| ---
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|
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| # EDEN-InceptionV3-CIFAR-10 — *Baseline – Standard Full Training (Reference Study)*
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|
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| > **Primary KPI:** EAG (Energy-to-Accuracy Gradient) — see Green Delta Table below.
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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)**.
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| It serves as the **Brute-Force Baseline** for the InceptionV3 architecture on CIFAR-10,
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| providing a transparent energy reference for EAG benchmarking against EDEN-optimized models.
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| **Applied Technique:** Baseline – Standard Full Training (Reference Study)
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|
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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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| | **Dataset** | CIFAR-10 — 60,000 images – 10 classes (32×32 px) |
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|
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| ## 🟢 Green Delta Table
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| *This is the reference baseline. Compare against EDEN-optimized models for EAG.*
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| | Metric | InceptionV3 Baseline | EDEN Optimized | Δ |
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| |---|---|---|---|
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| | Accuracy | See CSV log | See SOTA repo | — |
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| | Total Energy (J) | See CSV log | See SOTA repo | — |
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| | **EAG Score** | — | See SOTA repo | ΔAcc/ΔJoules |
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|
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| ## E2AM Algorithm — Applied Phase
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| Standard full fine-tuning used as the **Brute-Force Baseline** for energy comparison. All layers trained from epoch 1 with a fixed learning rate and no gradient accumulation. Included for transparent EAG benchmarking.
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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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| ```
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|
|
