Datasets:

Shanmuk4622
/

E2AM_EfficientNetV2_S

---
license: apache-2.0
language:
- en
tags:
- energy-aware-training
- image-classification
- model-efficiency
- ablation-study
- effnetv2_s
datasets:
- cifar10
- cifar100
- tiny-imagenet
library_name: pytorch
pretty_name: 'E2AM ablation: EfficientNetV2-S'
---


# E2AM Ablation Results: EfficientNetV2-S

Energy-aware training ablation study for **EfficientNetV2-S** across three image-classification datasets: **CIFAR-10**, **CIFAR-100**, and **Tiny-ImageNet**.

Each dataset has 15 training variants (8 individual-method M0..M7, 7 cumulative ablation C0..C6) at 50 epochs, plus a 5-variant deployment pipeline (FP32 baseline, structured pruning, pruning+finetune, INT8 quantization, pruned+INT8).

**Status**: 45 completed variants, 0 partial.

## Quick links

- [Methodology](#methodology)
- [Headline results](#headline-results)
- [Cross-dataset comparison](#cross-dataset-comparison)
- [Per-dataset results](#per-dataset-results)
- [Deployment results](#deployment-results)
- [Reproducibility](#reproducibility)

## Headline results

| Dataset | Best variant | Top-1 | Top-5 | Energy (kWh) | CO₂ (kg) | Time (sec) |
|---|---|---|---|---|---|---|
| CIFAR-10 | C5_cache_amp_gradaccum_adaptivelr_l1 | 0.9040 | 0.9977 | 0.1006 | 0.0478 | 4909 |
| CIFAR-100 | C4_cache_amp_gradaccum_adaptivelr | 0.7096 | 0.9251 | 0.0954 | 0.0453 | 4587 |
| Tiny-ImageNet | M7_full_e2am | 0.5805 | 0.8162 | 0.2005 | 0.0952 | 9683 |

## Cross-dataset comparison

How the same training variants behave across CIFAR-10, CIFAR-100, and Tiny-ImageNet.

### Accuracy By Variant Across Datasets

![accuracy_by_variant_across_datasets](comparison_plots/cross_dataset/accuracy_by_variant_across_datasets.png)

### Energy By Variant Across Datasets

![energy_by_variant_across_datasets](comparison_plots/cross_dataset/energy_by_variant_across_datasets.png)

## Per-dataset results

### CIFAR-10

**M-matrix (individual methods)**

| Variant | Epochs | Top-1 | Top-5 | Energy (kWh) | CO₂ (kg) | Time (s) | Status |
|---|---|---|---|---|---|---|---|
| M0_baseline_fp32 | 50 | 0.7565 | 0.9839 | 0.2080 | 0.0988 | 9714 | completed |
| M1_cache_only | 50 | 0.7582 | 0.9851 | 0.1963 | 0.0932 | 9440 | completed |
| M2_amp_only | 50 | 0.8013 | 0.9909 | 0.0986 | 0.0469 | 4775 | completed |
| M3_grad_accum_only | 50 | 0.8036 | 0.9903 | 0.1955 | 0.0928 | 9403 | completed |
| M4_l1_sparsity_only | 50 | 0.7394 | 0.9837 | 0.2097 | 0.0996 | 9834 | completed |
| M5_adaptive_lr_only | 50 | 0.8841 | 0.9969 | 0.1963 | 0.0933 | 9454 | completed |
| M6_eag_only | 50 | 0.7590 | 0.9854 | 0.2080 | 0.0988 | 9713 | completed |
| M7_full_e2am | 50 | 0.8974 | 0.9967 | 0.1005 | 0.0477 | 4847 | completed |

**C-matrix (cumulative ablation)**

| Variant | Epochs | Top-1 | Top-5 | Energy (kWh) | CO₂ (kg) | Time (s) | Status |
|---|---|---|---|---|---|---|---|
| C0_baseline | 50 | 0.7569 | 0.9865 | 0.1988 | 0.0944 | 9591 | completed |
| C1_cache | 50 | 0.7472 | 0.9862 | 0.1985 | 0.0943 | 9556 | completed |
| C2_cache_amp | 50 | 0.8068 | 0.9913 | 0.0988 | 0.0469 | 4824 | completed |
| C3_cache_amp_gradaccum | 50 | 0.8345 | 0.9937 | 0.0988 | 0.0469 | 4810 | completed |
| C4_cache_amp_gradaccum_adaptivelr | 50 | 0.8973 | 0.9971 | 0.0989 | 0.0470 | 4825 | completed |
| C5_cache_amp_gradaccum_adaptivelr_l1 | 50 | 0.9040 | 0.9977 | 0.1006 | 0.0478 | 4909 | completed |
| C6_full_e2am | 50 | 0.9040 | 0.9977 | 0.1007 | 0.0478 | 4915 | completed |

![accuracy_bar.png](comparison_plots/cifar10/accuracy_bar.png)

![energy_bar.png](comparison_plots/cifar10/energy_bar.png)

![co2_bar.png](comparison_plots/cifar10/co2_bar.png)

![accuracy_vs_energy_scatter.png](comparison_plots/cifar10/accuracy_vs_energy_scatter.png)

### CIFAR-100

**M-matrix (individual methods)**

| Variant | Epochs | Top-1 | Top-5 | Energy (kWh) | CO₂ (kg) | Time (s) | Status |
|---|---|---|---|---|---|---|---|
| M0_baseline_fp32 | 50 | 0.3900 | 0.7104 | 0.1990 | 0.0945 | 9332 | completed |
| M1_cache_only | 50 | 0.4190 | 0.7465 | 0.1992 | 0.0946 | 9689 | completed |
| M2_amp_only | 50 | 0.5240 | 0.8238 | 0.0956 | 0.0454 | 4608 | completed |
| M3_grad_accum_only | 50 | 0.5408 | 0.8365 | 0.1984 | 0.0943 | 9629 | completed |
| M4_l1_sparsity_only | 50 | 0.4378 | 0.7671 | 0.2019 | 0.0959 | 9629 | completed |
| M5_adaptive_lr_only | 50 | 0.5975 | 0.8641 | 0.1995 | 0.0948 | 9678 | completed |
| M6_eag_only | 50 | 0.4010 | 0.7281 | 0.1999 | 0.0950 | 9520 | completed |
| M7_full_e2am | 50 | 0.7090 | 0.9254 | 0.1025 | 0.0487 | 4970 | completed |

**C-matrix (cumulative ablation)**

| Variant | Epochs | Top-1 | Top-5 | Energy (kWh) | CO₂ (kg) | Time (s) | Status |
|---|---|---|---|---|---|---|---|
| C0_baseline | 50 | 0.4075 | 0.7359 | 0.1999 | 0.0950 | 9529 | completed |
| C1_cache | 50 | 0.4502 | 0.7700 | 0.1998 | 0.0949 | 9533 | completed |
| C2_cache_amp | 50 | 0.5411 | 0.8401 | 0.0982 | 0.0466 | 4756 | completed |
| C3_cache_amp_gradaccum | 50 | 0.6111 | 0.8775 | 0.0953 | 0.0453 | 4578 | completed |
| C4_cache_amp_gradaccum_adaptivelr | 50 | 0.7096 | 0.9251 | 0.0954 | 0.0453 | 4587 | completed |
| C5_cache_amp_gradaccum_adaptivelr_l1 | 50 | 0.7016 | 0.9205 | 0.0968 | 0.0460 | 4672 | completed |
| C6_full_e2am | 50 | 0.7017 | 0.9191 | 0.0999 | 0.0475 | 4812 | completed |

![accuracy_bar.png](comparison_plots/cifar100/accuracy_bar.png)

![energy_bar.png](comparison_plots/cifar100/energy_bar.png)

![co2_bar.png](comparison_plots/cifar100/co2_bar.png)

![accuracy_vs_energy_scatter.png](comparison_plots/cifar100/accuracy_vs_energy_scatter.png)

### Tiny-ImageNet

**M-matrix (individual methods)**

| Variant | Epochs | Top-1 | Top-5 | Energy (kWh) | CO₂ (kg) | Time (s) | Status |
|---|---|---|---|---|---|---|---|
| M0_baseline_fp32 | 50 | 0.2462 | 0.5170 | 0.3952 | 0.1877 | 18949 | completed |
| M1_cache_only | 50 | 0.2451 | 0.5128 | 0.3944 | 0.1873 | 19008 | completed |
| M2_amp_only | 50 | 0.3541 | 0.6305 | 0.1937 | 0.0920 | 9387 | completed |
| M3_grad_accum_only | 50 | 0.3467 | 0.6401 | 0.3921 | 0.1863 | 18791 | completed |
| M4_l1_sparsity_only | 50 | 0.2440 | 0.5172 | 0.4054 | 0.1926 | 19502 | completed |
| M5_adaptive_lr_only | 50 | 0.4611 | 0.7419 | 0.3979 | 0.1890 | 19208 | completed |
| M6_eag_only | 50 | 0.2606 | 0.5416 | 0.3992 | 0.1896 | 19319 | completed |
| M7_full_e2am | 50 | 0.5805 | 0.8162 | 0.2005 | 0.0952 | 9683 | completed |

**C-matrix (cumulative ablation)**

| Variant | Epochs | Top-1 | Top-5 | Energy (kWh) | CO₂ (kg) | Time (s) | Status |
|---|---|---|---|---|---|---|---|
| C0_baseline | 50 | 0.2475 | 0.5199 | 0.4119 | 0.1957 | 19800 | completed |
| C1_cache | 50 | 0.2502 | 0.5186 | 0.4014 | 0.1907 | 19312 | completed |
| C2_cache_amp | 50 | 0.3465 | 0.6300 | 0.1980 | 0.0940 | 9633 | completed |
| C3_cache_amp_gradaccum | 50 | 0.4436 | 0.7242 | 0.2012 | 0.0956 | 9766 | completed |
| C4_cache_amp_gradaccum_adaptivelr | 50 | 0.5738 | 0.8162 | 0.2026 | 0.0963 | 9831 | completed |
| C5_cache_amp_gradaccum_adaptivelr_l1 | 50 | 0.5708 | 0.8125 | 0.2053 | 0.0975 | 9943 | completed |
| C6_full_e2am | 50 | 0.5708 | 0.8125 | 0.2053 | 0.0975 | 9939 | completed |

![accuracy_bar.png](comparison_plots/tiny_imagenet/accuracy_bar.png)

![energy_bar.png](comparison_plots/tiny_imagenet/energy_bar.png)

![co2_bar.png](comparison_plots/tiny_imagenet/co2_bar.png)

![accuracy_vs_energy_scatter.png](comparison_plots/tiny_imagenet/accuracy_vs_energy_scatter.png)

## Deployment results

No deployment results in this repo yet.

## Methodology

**Model**: EfficientNetV2-S (~20.2M params).

**Training protocol**: from scratch, SGD with momentum 0.9, weight decay 5e-4, initial LR 0.1, 50 epochs, 1 warmup epoch. All variants share the same protocol so ablation comparison stays apples-to-apples across the matrix.

**Input**: native dataset resolution upsampled to 128x128 in-model via `nn.Upsample` (FX-traceable to keep D3/D4 INT8 quantization possible).

**Optimization toggles** (the 5 individual methods and their cumulative combinations):

| Method | Mechanism |
|---|---|
| Tensor cache | Training images held in RAM as a normalized float tensor |
| AMP | torch.cuda.amp.autocast + GradScaler |
| Grad accum (x2) | Accumulate gradients across 2 mini-batches |
| L1 sparsity | Lambda * sum(|w_i|) added to loss with lambda=1e-8 |
| Cosine LR | lr(t) = lr_max * 0.5 * (1 + cos(pi*t/T)) |
| EAG early-stop | Energy-Aware Gain: stop when accuracy gain per joule plateaus |

**Energy measurement**: GPU power sampled at 1 Hz via `nvidia-smi --query-gpu=power.draw`. Energy = trapezoidal integration over power-vs-time. CO₂ = energy_kWh * 0.475 (global average grid intensity).

**Hardware**: Single NVIDIA T4 (14.5 GB) on Kaggle.

## Repository structure

```
runs/
  cifar10/
  cifar100/
  tiny_imagenet/
    individual_methods/M0..M7/   (history.csv, metrics_summary.json,
                                  best_model.pt, last_model.pt, config.yaml)
    cumulative_ablation/C0..C6/  (same)
paper_tables/                     (6 unified CSV tables)
comparison_plots/<dataset>/       (per-dataset plots)
comparison_plots/cross_dataset/   (cross-dataset plots)
README.md                         (this file)
```

## Reproducibility

Each variant directory has a `config.yaml` with the exact configuration used. To reproduce:

1. `huggingface-cli download Shanmuk4622/E2AM_EfficientNetV2_S --repo-type dataset`
2. Load the `e2am.py` library and call the appropriate config factory
3. Run `e2am.train_one_run(cfg)`

## Limitations

- Energy measurement is GPU-only (via nvidia-smi); CPU/memory power not included
- Pruning is mask-based; no wall-clock speedup without sparsity-aware runtime
- INT8 (D3/D4) is CPU FX static quantization (fbgemm); may fail on transformer blocks. Failures logged in metrics.json rather than crashing.
- Single-T4 reproduction; multi-GPU not validated
- SGD@0.1 is suboptimal for some architectures; the paper compares variant-to-variant deltas which remain meaningful regardless

## Citation

```bibtex
@misc{e2am_ablation_effnetv2s,
  title  = {E2AM: Energy-Aware Adaptive Model Training Ablation Study (EfficientNetV2-S)},
  author = {Shanmuk},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/datasets/Shanmuk4622/E2AM_EfficientNetV2_S}},
}
```

---

_This README was auto-generated on 2026-06-06 12:40 UTC._
_Source repo: Shanmuk4622/E2AM_EfficientNetV2_S_