EDEN-Core-Scripts — E2AM Framework Repository

Project EDEN (Energy-Driven Evolution of Networks) — The complete algorithmic toolkit for Green SOTA image classification research.

Why EDEN?

As deep learning models scale exponentially, the carbon footprint of training has reached unsustainable levels. Project EDEN introduces the EAG (Energy-to-Accuracy Gradient) as the primary KPI — shifting the paradigm from chasing raw accuracy to optimising Green SOTA.

Profiling Environment

Component	Specification
GPU	NVIDIA GeForce GTX 1080 Ti (11 GB VRAM, 250 W TDP)
CPU	Intel Xeon W-2125 (4 cores / 8 threads @ 4.00 GHz)
RAM	63.66 GB System RAM
OS	Windows 10

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📊 Collection Overview

Energy vs Accuracy — All Models

SOTA Optimized (green) · Baseline (grey) · EDEN Classic (blue)

EAG Leaderboard — Ranked by Green Efficiency

CO₂ Emissions — Baseline vs EDEN Classic

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The E2AM Algorithm

Phase 1 — Zero-Overhead Initialization

Dataset pre-loaded into pinned System RAM before training — eliminates disk I/O power spikes.

Phase 2 — Two-Stage Energy-Aware Training

1. Frozen Head Training — Only the classification head trains for E_unfreeze epochs.
2. Progressive Unfreezing — All layers unlock at E_unfreeze; LR decayed (×0.1).
3. Gradient Accumulation — Simulates large batch sizes without VRAM spikes.
4. AMP — torch.cuda.amp.autocast() halves bandwidth per backward pass.
5. Sparse L1 Penalty — L_total = CrossEntropy + λ·Σ|W_trainable|
6. EAG Early-Exit — Terminates if EAG < γ_EAG for 3 consecutive epochs.

Phase 3 — Hardware-Aware Deployment (Post-Training)

Saliency-energy pruning · INT8 quantization · Dynamic depth routing

EAG — The Expert KPI

EAG = ΔAccuracy / ΔJoules

A higher EAG = more learning per unit of carbon footprint.

Scripts in This Repository

• eden_chart_push.py
• eden_check_hf.py
• eden_fix_missing_repos.py
• eden_hf_upload.py
• eden_upload_fast.py
• eden_upload_weights.py
• test1\Algo_CIFAR_100_EfficientNet.py
• test1\Algo_CIFAR_100_MobileViTv3.py
• test1\Algo_CIFAR_100_convneXt.py
• test1\Algo_CIFAR_10_EfficientNet.py
• test1\Algo_CIFAR_10_MobileViTv3.py
• test1\Algo_CIFAR_10_convneXt.py
• test1\Algo_ImageNet_EfficientNet.py
• test1\Algo_ImageNet_convnext.py
• test1\Algo_ImageNet_mobilevit3.py
• test1\mobilevit_model.py
• test3\eden_AlexNet_CIFAR10.py
• test3\eden_AlexNet_CIFAR100.py
• test3\eden_AlexNet_ImageNet.py
• test3\eden_DenseNet_121_CIFAR10.py
• test3\eden_DenseNet_121_CIFAR100.py
• test3\eden_DenseNet_121_ImageNet.py
• test3\eden_InceptionV3_CIFAR10.py
• test3\eden_InceptionV3_CIFAR100.py
• test3\eden_InceptionV3_ImageNet.py
• test3\eden_ResNet18_CIFAR10.py
• test3\eden_ResNet18_CIFAR100.py
• test3\eden_ResNet18_ImageNet.py
• test3\eden_ResNet50_CIFAR10.py
• test3\eden_ResNet50_CIFAR100.py
• test3\eden_ResNet50_ImageNet.py
• test3\eden_UNet_CIFAR10.py
• test3\eden_UNet_CIFAR100.py
• test3\eden_UNet_ImageNet.py
• test3\eden_VGG16_CIFAR10.py
• test3\eden_VGG16_CIFAR100.py
• test3\eden_VGG16_ImageNet.py

Cite This Research

@misc{eden2025,
  title     = {Project EDEN: Energy-Driven Evolution of Networks},
  author    = {EDEN Research Team},
  year      = {2025},
  note      = {Hugging Face: Shanmuk4622},
  url       = {https://huggingface.co/Shanmuk4622}
}