---
license: apache-2.0
language:
- en
tags:
- energy-aware-training
- image-classification
- model-efficiency
- ablation-study
- resnet50
datasets:
- cifar10
- cifar100
- tiny-imagenet
library_name: pytorch
pretty_name: 'E2AM ablation: ResNet-50'
---


# E2AM Ablation Results: ResNet-50

Energy-aware training ablation study for **ResNet-50** 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 | M5_adaptive_lr_only | 0.9397 | — | 0.1556 | 0.0739 | 7368 |
| CIFAR-100 | M5_adaptive_lr_only | 0.7482 | 0.9358 | 0.1587 | 0.0754 | 7578 |
| Tiny-ImageNet | C5_cache_amp_gradaccum_adaptivelr_l1 | 0.6742 | 0.8651 | 0.5231 | 0.2485 | 24653 |

## 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 | 0.8298 | — | 0.1590 | 0.0755 | 7588 | completed |
| M1_cache_only | 50 | 0.8543 | — | 0.1561 | 0.0741 | 7369 | completed |
| M2_amp_only | 50 | 0.8744 | — | 0.0622 | 0.0295 | 3006 | completed |
| M3_grad_accum_only | 50 | 0.8720 | — | 0.1552 | 0.0737 | 7332 | completed |
| M4_l1_sparsity_only | 50 | 0.8387 | — | 0.1578 | 0.0750 | 7447 | completed |
| M5_adaptive_lr_only | 50 | 0.9397 | — | 0.1556 | 0.0739 | 7368 | completed |
| M6_eag_only | 50 | 0.8452 | — | 0.1611 | 0.0765 | 7654 | completed |
| M7_full_e2am | 50 | 0.9088 | — | 0.0664 | 0.0315 | 3192 | completed |

**C-matrix (cumulative ablation)**

| Variant | Epochs | Top-1 | Top-5 | Energy (kWh) | CO₂ (kg) | Time (s) | Status |
|---|---|---|---|---|---|---|---|
| C0_baseline | 50 | 0.8442 | — | 0.1747 | 0.0830 | 8464 | completed |
| C1_cache | 50 | 0.8452 | — | 0.1752 | 0.0832 | 8495 | completed |
| C2_cache_amp | 50 | 0.8595 | — | 0.0663 | 0.0315 | 3189 | completed |
| C3_cache_amp_gradaccum | 50 | 0.8700 | — | 0.0713 | 0.0339 | 3479 | completed |
| C4_cache_amp_gradaccum_adaptivelr | 50 | 0.9084 | — | 0.0711 | 0.0338 | 3476 | completed |
| C5_cache_amp_gradaccum_adaptivelr_l1 | 50 | 0.9151 | — | 0.0718 | 0.0341 | 3504 | completed |
| C6_full_e2am | 50 | 0.9151 | — | 0.0717 | 0.0340 | 3503 | 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 | 0.5913 | 0.8446 | 0.1569 | 0.0745 | 7414 | completed |
| M1_cache_only | 50 | 0.5947 | 0.8582 | 0.1567 | 0.0744 | 7415 | completed |
| M2_amp_only | 50 | 0.6081 | 0.8652 | 0.0702 | 0.0334 | 3377 | completed |
| M3_grad_accum_only | 50 | 0.6228 | 0.8766 | 0.1559 | 0.0741 | 7380 | completed |
| M4_l1_sparsity_only | 50 | 0.5897 | 0.8576 | 0.1581 | 0.0751 | 7475 | completed |
| M5_adaptive_lr_only | 50 | 0.7482 | 0.9358 | 0.1587 | 0.0754 | 7578 | completed |
| M6_eag_only | 50 | 0.5821 | 0.8440 | 0.1565 | 0.0743 | 7412 | completed |
| M7_full_e2am | 50 | 0.7008 | 0.9087 | 0.0648 | 0.0308 | 3123 | completed |

**C-matrix (cumulative ablation)**

| Variant | Epochs | Top-1 | Top-5 | Energy (kWh) | CO₂ (kg) | Time (s) | Status |
|---|---|---|---|---|---|---|---|
| C0_baseline | 50 | 0.5822 | 0.8628 | 0.1657 | 0.0787 | 7918 | completed |
| C1_cache | 50 | 0.5916 | 0.8505 | 0.1621 | 0.0770 | 7754 | completed |
| C2_cache_amp | 50 | 0.6307 | 0.8665 | 0.0670 | 0.0318 | 3233 | completed |
| C3_cache_amp_gradaccum | 50 | 0.6306 | 0.8761 | 0.0659 | 0.0313 | 3188 | completed |
| C4_cache_amp_gradaccum_adaptivelr | 50 | 0.6918 | 0.9042 | 0.0659 | 0.0313 | 3190 | completed |
| C5_cache_amp_gradaccum_adaptivelr_l1 | 50 | 0.6753 | 0.8974 | 0.0667 | 0.0317 | 3225 | completed |
| C6_full_e2am | 50 | 0.6753 | 0.8974 | 0.0670 | 0.0318 | 3224 | 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 | 0.4187 | 0.6629 | 1.2832 | 0.6095 | 60871 | completed |
| M1_cache_only | 50 | 0.4027 | 0.6637 | 1.2088 | 0.5742 | 57541 | completed |
| M2_amp_only | 50 | 0.4889 | 0.7445 | 0.5128 | 0.2436 | 24268 | completed |
| M3_grad_accum_only | 50 | 0.5141 | 0.7502 | 1.3058 | 0.6203 | 61979 | completed |
| M4_l1_sparsity_only | 50 | 0.4112 | 0.6874 | 1.2378 | 0.5880 | 58649 | completed |
| M5_adaptive_lr_only | 50 | 0.6243 | 0.8426 | 1.1917 | 0.5661 | 56539 | completed |
| M6_eag_only | 50 | 0.3996 | 0.6543 | 1.2605 | 0.5987 | 59534 | completed |
| M7_full_e2am | 50 | 0.6641 | 0.8580 | 0.5423 | 0.2576 | 25681 | completed |

**C-matrix (cumulative ablation)**

| Variant | Epochs | Top-1 | Top-5 | Energy (kWh) | CO₂ (kg) | Time (s) | Status |
|---|---|---|---|---|---|---|---|
| C0_baseline | 50 | 0.4121 | 0.6262 | 1.3775 | 0.6543 | 65154 | completed |
| C1_cache | 50 | 0.4213 | 0.7007 | 1.3177 | 0.6259 | 62533 | completed |
| C2_cache_amp | 50 | 0.4993 | 0.7211 | 0.4929 | 0.2341 | 22910 | completed |
| C3_cache_amp_gradaccum | 50 | 0.5505 | 0.7980 | 0.5049 | 0.2398 | 23604 | completed |
| C4_cache_amp_gradaccum_adaptivelr | 50 | 0.6667 | 0.8607 | 0.5437 | 0.2583 | 25771 | completed |
| C5_cache_amp_gradaccum_adaptivelr_l1 | 50 | 0.6742 | 0.8651 | 0.5231 | 0.2485 | 24653 | completed |
| C6_full_e2am | 49 | 0.6716 | 0.8621 | 0.5163 | 0.2453 | 24352 | 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

See `paper_tables/deployment_results_table.csv`.

## Methodology

**Model**: ResNet-50 (~23.5M 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 32x32 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_ResNet50 --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_resnet50,
  title  = {E2AM: Energy-Aware Adaptive Model Training Ablation Study (ResNet-50)},
  author = {Shanmuk},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/datasets/Shanmuk4622/E2AM_ResNet50}},
}
```

---

_This README was auto-generated on 2026-07-02 08:14 UTC._
_Source repo: Shanmuk4622/E2AM_ResNet50_