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README.md

@@ -16,10 +16,9 @@ tags:
 > 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*.
+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 |
@@ -29,42 +28,52 @@ chasing raw accuracy to optimising *Green SOTA*.
 | **RAM** | 63.66 GB System RAM |
 | **OS**  | Windows 10 |
 
-## The E2AM Algorithm — All Three Phases
+---
+
+## 📊 Collection Overview
+
+### Energy vs Accuracy — All Models
+*SOTA Optimized (green) · Baseline (grey) · EDEN Classic (blue)*
+
+![Energy vs Accuracy](energy_accuracy_overview.png)
+
+### EAG Leaderboard — Ranked by Green Efficiency
+![EAG Leaderboard](eag_leaderboard.png)
+
+### CO₂ Emissions — Baseline vs EDEN Classic
+![CO2 Comparison](co2_comparison.png)
+
+---
+
+## The E2AM Algorithm
 
 ### Phase 1 — Zero-Overhead Initialization
-Dataset pre-loaded into **pinned System RAM** before training begins.
-This eliminates disk I/O power spikes that would otherwise inflate energy readings
-and distort EAG comparisons between architectures.
+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 the first
-   `E_unfreeze` epochs. The backbone consumes no backward-pass energy.
-2. **Progressive Unfreezing** — At epoch `E_unfreeze`, all layers unlock.
-   Learning rate is decayed (`LR × 0.1`) for stable fine-tuning.
-3. **Gradient Accumulation** — Gradients accumulated over N micro-batches,
-   simulating large batch sizes without VRAM spikes.
-4. **AMP (Automated Mixed Precision)** — `torch.cuda.amp.autocast()` halves
-   bandwidth per backward pass.
+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** — Training terminates if `EAG < γ_EAG` for 3 consecutive
-   epochs, preventing wasted compute.
+6. **EAG Early-Exit** — Terminates if `EAG < γ_EAG` for 3 consecutive epochs.
 
 ### Phase 3 — Hardware-Aware Deployment *(Post-Training)*
-- **Saliency-Energy Pruning** — Filters with lowest `∂Accuracy/∂W ÷ Energy_cost`
-  are pruned.
-- **INT8 Quantization** — Weights converted for edge-deployment readiness.
-- **Dynamic Depth Routing** — Simple images bypass the middle 50 % of layers
-  via residual skip connections, slashing inference energy.
+Saliency-energy pruning · INT8 quantization · Dynamic depth routing
 
 ## EAG — The Expert KPI
 ```
 EAG = ΔAccuracy / ΔJoules
 ```
-EAG allows apples-to-apples comparison of any two models regardless of
-architecture family. A higher EAG = more learning per unit of carbon footprint.
+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`
@@ -103,7 +112,7 @@ architecture family. A higher EAG = more learning per unit of carbon footprint.
   title     = {Project EDEN: Energy-Driven Evolution of Networks},
   author    = {EDEN Research Team},
   year      = {2025},
-  note      = {Hugging Face Organization: ProjectEDEN},
+  note      = {Hugging Face: Shanmuk4622},
   url       = {https://huggingface.co/Shanmuk4622}
 }
 ```