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_