Generate dataset card + paper tables + plots (21 files)
Browse files- README.md +237 -0
- comparison_plots/cifar10/accuracy_bar.png +2 -2
- comparison_plots/cifar10/accuracy_vs_energy_scatter.png +2 -2
- comparison_plots/cifar10/co2_bar.png +2 -2
- comparison_plots/cifar10/energy_bar.png +2 -2
- comparison_plots/cifar100/accuracy_bar.png +2 -2
- comparison_plots/cifar100/accuracy_vs_energy_scatter.png +2 -2
- comparison_plots/cifar100/co2_bar.png +2 -2
- comparison_plots/cifar100/energy_bar.png +2 -2
- comparison_plots/cross_dataset/accuracy_by_variant_across_datasets.png +3 -0
- comparison_plots/cross_dataset/energy_by_variant_across_datasets.png +3 -0
- comparison_plots/tiny_imagenet/accuracy_bar.png +2 -2
- comparison_plots/tiny_imagenet/accuracy_vs_energy_scatter.png +2 -2
- comparison_plots/tiny_imagenet/co2_bar.png +2 -2
- comparison_plots/tiny_imagenet/energy_bar.png +2 -2
- paper_tables/all_metrics_summary.csv +46 -16
- paper_tables/cumulative_ablation_table.csv +22 -8
- paper_tables/equal_epoch_comparison.csv +46 -5
- paper_tables/individual_method_study.csv +25 -9
- paper_tables/training_results_table.csv +46 -16
README.md
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| 1 |
+
---
|
| 2 |
+
license: apache-2.0
|
| 3 |
+
language:
|
| 4 |
+
- en
|
| 5 |
+
tags:
|
| 6 |
+
- energy-aware-training
|
| 7 |
+
- image-classification
|
| 8 |
+
- model-efficiency
|
| 9 |
+
- ablation-study
|
| 10 |
+
- effnetv2_s
|
| 11 |
+
datasets:
|
| 12 |
+
- cifar10
|
| 13 |
+
- cifar100
|
| 14 |
+
- tiny-imagenet
|
| 15 |
+
library_name: pytorch
|
| 16 |
+
pretty_name: 'E2AM ablation: EfficientNetV2-S'
|
| 17 |
+
---
|
| 18 |
+
|
| 19 |
+
|
| 20 |
+
# E2AM Ablation Results: EfficientNetV2-S
|
| 21 |
+
|
| 22 |
+
Energy-aware training ablation study for **EfficientNetV2-S** across three image-classification datasets: **CIFAR-10**, **CIFAR-100**, and **Tiny-ImageNet**.
|
| 23 |
+
|
| 24 |
+
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).
|
| 25 |
+
|
| 26 |
+
**Status**: 45 completed variants, 0 partial.
|
| 27 |
+
|
| 28 |
+
## Quick links
|
| 29 |
+
|
| 30 |
+
- [Methodology](#methodology)
|
| 31 |
+
- [Headline results](#headline-results)
|
| 32 |
+
- [Cross-dataset comparison](#cross-dataset-comparison)
|
| 33 |
+
- [Per-dataset results](#per-dataset-results)
|
| 34 |
+
- [Deployment results](#deployment-results)
|
| 35 |
+
- [Reproducibility](#reproducibility)
|
| 36 |
+
|
| 37 |
+
## Headline results
|
| 38 |
+
|
| 39 |
+
| Dataset | Best variant | Top-1 | Top-5 | Energy (kWh) | CO₂ (kg) | Time (sec) |
|
| 40 |
+
|---|---|---|---|---|---|---|
|
| 41 |
+
| CIFAR-10 | C5_cache_amp_gradaccum_adaptivelr_l1 | 0.9040 | 0.9977 | 0.1006 | 0.0478 | 4909 |
|
| 42 |
+
| CIFAR-100 | C4_cache_amp_gradaccum_adaptivelr | 0.7096 | 0.9251 | 0.0954 | 0.0453 | 4587 |
|
| 43 |
+
| Tiny-ImageNet | M7_full_e2am | 0.5805 | 0.8162 | 0.2005 | 0.0952 | 9683 |
|
| 44 |
+
|
| 45 |
+
## Cross-dataset comparison
|
| 46 |
+
|
| 47 |
+
How the same training variants behave across CIFAR-10, CIFAR-100, and Tiny-ImageNet.
|
| 48 |
+
|
| 49 |
+
### Accuracy By Variant Across Datasets
|
| 50 |
+
|
| 51 |
+
|
| 52 |
+
|
| 53 |
+
### Energy By Variant Across Datasets
|
| 54 |
+
|
| 55 |
+
|
| 56 |
+
|
| 57 |
+
## Per-dataset results
|
| 58 |
+
|
| 59 |
+
### CIFAR-10
|
| 60 |
+
|
| 61 |
+
**M-matrix (individual methods)**
|
| 62 |
+
|
| 63 |
+
| Variant | Epochs | Top-1 | Top-5 | Energy (kWh) | CO₂ (kg) | Time (s) | Status |
|
| 64 |
+
|---|---|---|---|---|---|---|---|
|
| 65 |
+
| M0_baseline_fp32 | 50 | 0.7565 | 0.9839 | 0.2080 | 0.0988 | 9714 | completed |
|
| 66 |
+
| M1_cache_only | 50 | 0.7582 | 0.9851 | 0.1963 | 0.0932 | 9440 | completed |
|
| 67 |
+
| M2_amp_only | 50 | 0.8013 | 0.9909 | 0.0986 | 0.0469 | 4775 | completed |
|
| 68 |
+
| M3_grad_accum_only | 50 | 0.8036 | 0.9903 | 0.1955 | 0.0928 | 9403 | completed |
|
| 69 |
+
| M4_l1_sparsity_only | 50 | 0.7394 | 0.9837 | 0.2097 | 0.0996 | 9834 | completed |
|
| 70 |
+
| M5_adaptive_lr_only | 50 | 0.8841 | 0.9969 | 0.1963 | 0.0933 | 9454 | completed |
|
| 71 |
+
| M6_eag_only | 50 | 0.7590 | 0.9854 | 0.2080 | 0.0988 | 9713 | completed |
|
| 72 |
+
| M7_full_e2am | 50 | 0.8974 | 0.9967 | 0.1005 | 0.0477 | 4847 | completed |
|
| 73 |
+
|
| 74 |
+
**C-matrix (cumulative ablation)**
|
| 75 |
+
|
| 76 |
+
| Variant | Epochs | Top-1 | Top-5 | Energy (kWh) | CO₂ (kg) | Time (s) | Status |
|
| 77 |
+
|---|---|---|---|---|---|---|---|
|
| 78 |
+
| C0_baseline | 50 | 0.7569 | 0.9865 | 0.1988 | 0.0944 | 9591 | completed |
|
| 79 |
+
| C1_cache | 50 | 0.7472 | 0.9862 | 0.1985 | 0.0943 | 9556 | completed |
|
| 80 |
+
| C2_cache_amp | 50 | 0.8068 | 0.9913 | 0.0988 | 0.0469 | 4824 | completed |
|
| 81 |
+
| C3_cache_amp_gradaccum | 50 | 0.8345 | 0.9937 | 0.0988 | 0.0469 | 4810 | completed |
|
| 82 |
+
| C4_cache_amp_gradaccum_adaptivelr | 50 | 0.8973 | 0.9971 | 0.0989 | 0.0470 | 4825 | completed |
|
| 83 |
+
| C5_cache_amp_gradaccum_adaptivelr_l1 | 50 | 0.9040 | 0.9977 | 0.1006 | 0.0478 | 4909 | completed |
|
| 84 |
+
| C6_full_e2am | 50 | 0.9040 | 0.9977 | 0.1007 | 0.0478 | 4915 | completed |
|
| 85 |
+
|
| 86 |
+
|
| 87 |
+
|
| 88 |
+
|
| 89 |
+
|
| 90 |
+
|
| 91 |
+
|
| 92 |
+
|
| 93 |
+
|
| 94 |
+
### CIFAR-100
|
| 95 |
+
|
| 96 |
+
**M-matrix (individual methods)**
|
| 97 |
+
|
| 98 |
+
| Variant | Epochs | Top-1 | Top-5 | Energy (kWh) | CO₂ (kg) | Time (s) | Status |
|
| 99 |
+
|---|---|---|---|---|---|---|---|
|
| 100 |
+
| M0_baseline_fp32 | 50 | 0.3900 | 0.7104 | 0.1990 | 0.0945 | 9332 | completed |
|
| 101 |
+
| M1_cache_only | 50 | 0.4190 | 0.7465 | 0.1992 | 0.0946 | 9689 | completed |
|
| 102 |
+
| M2_amp_only | 50 | 0.5240 | 0.8238 | 0.0956 | 0.0454 | 4608 | completed |
|
| 103 |
+
| M3_grad_accum_only | 50 | 0.5408 | 0.8365 | 0.1984 | 0.0943 | 9629 | completed |
|
| 104 |
+
| M4_l1_sparsity_only | 50 | 0.4378 | 0.7671 | 0.2019 | 0.0959 | 9629 | completed |
|
| 105 |
+
| M5_adaptive_lr_only | 50 | 0.5975 | 0.8641 | 0.1995 | 0.0948 | 9678 | completed |
|
| 106 |
+
| M6_eag_only | 50 | 0.4010 | 0.7281 | 0.1999 | 0.0950 | 9520 | completed |
|
| 107 |
+
| M7_full_e2am | 50 | 0.7090 | 0.9254 | 0.1025 | 0.0487 | 4970 | completed |
|
| 108 |
+
|
| 109 |
+
**C-matrix (cumulative ablation)**
|
| 110 |
+
|
| 111 |
+
| Variant | Epochs | Top-1 | Top-5 | Energy (kWh) | CO₂ (kg) | Time (s) | Status |
|
| 112 |
+
|---|---|---|---|---|---|---|---|
|
| 113 |
+
| C0_baseline | 50 | 0.4075 | 0.7359 | 0.1999 | 0.0950 | 9529 | completed |
|
| 114 |
+
| C1_cache | 50 | 0.4502 | 0.7700 | 0.1998 | 0.0949 | 9533 | completed |
|
| 115 |
+
| C2_cache_amp | 50 | 0.5411 | 0.8401 | 0.0982 | 0.0466 | 4756 | completed |
|
| 116 |
+
| C3_cache_amp_gradaccum | 50 | 0.6111 | 0.8775 | 0.0953 | 0.0453 | 4578 | completed |
|
| 117 |
+
| C4_cache_amp_gradaccum_adaptivelr | 50 | 0.7096 | 0.9251 | 0.0954 | 0.0453 | 4587 | completed |
|
| 118 |
+
| C5_cache_amp_gradaccum_adaptivelr_l1 | 50 | 0.7016 | 0.9205 | 0.0968 | 0.0460 | 4672 | completed |
|
| 119 |
+
| C6_full_e2am | 50 | 0.7017 | 0.9191 | 0.0999 | 0.0475 | 4812 | completed |
|
| 120 |
+
|
| 121 |
+
|
| 122 |
+
|
| 123 |
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|
| 124 |
+
|
| 125 |
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|
| 126 |
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|
| 127 |
+
|
| 128 |
+
|
| 129 |
+
### Tiny-ImageNet
|
| 130 |
+
|
| 131 |
+
**M-matrix (individual methods)**
|
| 132 |
+
|
| 133 |
+
| Variant | Epochs | Top-1 | Top-5 | Energy (kWh) | CO₂ (kg) | Time (s) | Status |
|
| 134 |
+
|---|---|---|---|---|---|---|---|
|
| 135 |
+
| M0_baseline_fp32 | 50 | 0.2462 | 0.5170 | 0.3952 | 0.1877 | 18949 | completed |
|
| 136 |
+
| M1_cache_only | 50 | 0.2451 | 0.5128 | 0.3944 | 0.1873 | 19008 | completed |
|
| 137 |
+
| M2_amp_only | 50 | 0.3541 | 0.6305 | 0.1937 | 0.0920 | 9387 | completed |
|
| 138 |
+
| M3_grad_accum_only | 50 | 0.3467 | 0.6401 | 0.3921 | 0.1863 | 18791 | completed |
|
| 139 |
+
| M4_l1_sparsity_only | 50 | 0.2440 | 0.5172 | 0.4054 | 0.1926 | 19502 | completed |
|
| 140 |
+
| M5_adaptive_lr_only | 50 | 0.4611 | 0.7419 | 0.3979 | 0.1890 | 19208 | completed |
|
| 141 |
+
| M6_eag_only | 50 | 0.2606 | 0.5416 | 0.3992 | 0.1896 | 19319 | completed |
|
| 142 |
+
| M7_full_e2am | 50 | 0.5805 | 0.8162 | 0.2005 | 0.0952 | 9683 | completed |
|
| 143 |
+
|
| 144 |
+
**C-matrix (cumulative ablation)**
|
| 145 |
+
|
| 146 |
+
| Variant | Epochs | Top-1 | Top-5 | Energy (kWh) | CO₂ (kg) | Time (s) | Status |
|
| 147 |
+
|---|---|---|---|---|---|---|---|
|
| 148 |
+
| C0_baseline | 50 | 0.2475 | 0.5199 | 0.4119 | 0.1957 | 19800 | completed |
|
| 149 |
+
| C1_cache | 50 | 0.2502 | 0.5186 | 0.4014 | 0.1907 | 19312 | completed |
|
| 150 |
+
| C2_cache_amp | 50 | 0.3465 | 0.6300 | 0.1980 | 0.0940 | 9633 | completed |
|
| 151 |
+
| C3_cache_amp_gradaccum | 50 | 0.4436 | 0.7242 | 0.2012 | 0.0956 | 9766 | completed |
|
| 152 |
+
| C4_cache_amp_gradaccum_adaptivelr | 50 | 0.5738 | 0.8162 | 0.2026 | 0.0963 | 9831 | completed |
|
| 153 |
+
| C5_cache_amp_gradaccum_adaptivelr_l1 | 50 | 0.5708 | 0.8125 | 0.2053 | 0.0975 | 9943 | completed |
|
| 154 |
+
| C6_full_e2am | 50 | 0.5708 | 0.8125 | 0.2053 | 0.0975 | 9939 | completed |
|
| 155 |
+
|
| 156 |
+
|
| 157 |
+
|
| 158 |
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|
| 159 |
+
|
| 160 |
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|
| 161 |
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|
| 162 |
+
|
| 163 |
+
|
| 164 |
+
## Deployment results
|
| 165 |
+
|
| 166 |
+
No deployment results in this repo yet.
|
| 167 |
+
|
| 168 |
+
## Methodology
|
| 169 |
+
|
| 170 |
+
**Model**: EfficientNetV2-S (~20.2M params).
|
| 171 |
+
|
| 172 |
+
**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.
|
| 173 |
+
|
| 174 |
+
**Input**: native dataset resolution upsampled to 128x128 in-model via `nn.Upsample` (FX-traceable to keep D3/D4 INT8 quantization possible).
|
| 175 |
+
|
| 176 |
+
**Optimization toggles** (the 5 individual methods and their cumulative combinations):
|
| 177 |
+
|
| 178 |
+
| Method | Mechanism |
|
| 179 |
+
|---|---|
|
| 180 |
+
| Tensor cache | Training images held in RAM as a normalized float tensor |
|
| 181 |
+
| AMP | torch.cuda.amp.autocast + GradScaler |
|
| 182 |
+
| Grad accum (x2) | Accumulate gradients across 2 mini-batches |
|
| 183 |
+
| L1 sparsity | Lambda * sum(|w_i|) added to loss with lambda=1e-8 |
|
| 184 |
+
| Cosine LR | lr(t) = lr_max * 0.5 * (1 + cos(pi*t/T)) |
|
| 185 |
+
| EAG early-stop | Energy-Aware Gain: stop when accuracy gain per joule plateaus |
|
| 186 |
+
|
| 187 |
+
**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).
|
| 188 |
+
|
| 189 |
+
**Hardware**: Single NVIDIA T4 (14.5 GB) on Kaggle.
|
| 190 |
+
|
| 191 |
+
## Repository structure
|
| 192 |
+
|
| 193 |
+
```
|
| 194 |
+
runs/
|
| 195 |
+
cifar10/
|
| 196 |
+
cifar100/
|
| 197 |
+
tiny_imagenet/
|
| 198 |
+
individual_methods/M0..M7/ (history.csv, metrics_summary.json,
|
| 199 |
+
best_model.pt, last_model.pt, config.yaml)
|
| 200 |
+
cumulative_ablation/C0..C6/ (same)
|
| 201 |
+
paper_tables/ (6 unified CSV tables)
|
| 202 |
+
comparison_plots/<dataset>/ (per-dataset plots)
|
| 203 |
+
comparison_plots/cross_dataset/ (cross-dataset plots)
|
| 204 |
+
README.md (this file)
|
| 205 |
+
```
|
| 206 |
+
|
| 207 |
+
## Reproducibility
|
| 208 |
+
|
| 209 |
+
Each variant directory has a `config.yaml` with the exact configuration used. To reproduce:
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|
| 211 |
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1. `huggingface-cli download Shanmuk4622/E2AM_EfficientNetV2_S --repo-type dataset`
|
| 212 |
+
2. Load the `e2am.py` library and call the appropriate config factory
|
| 213 |
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3. Run `e2am.train_one_run(cfg)`
|
| 214 |
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| 215 |
+
## Limitations
|
| 216 |
+
|
| 217 |
+
- Energy measurement is GPU-only (via nvidia-smi); CPU/memory power not included
|
| 218 |
+
- Pruning is mask-based; no wall-clock speedup without sparsity-aware runtime
|
| 219 |
+
- INT8 (D3/D4) is CPU FX static quantization (fbgemm); may fail on transformer blocks. Failures logged in metrics.json rather than crashing.
|
| 220 |
+
- Single-T4 reproduction; multi-GPU not validated
|
| 221 |
+
- SGD@0.1 is suboptimal for some architectures; the paper compares variant-to-variant deltas which remain meaningful regardless
|
| 222 |
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| 223 |
+
## Citation
|
| 224 |
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|
| 225 |
+
```bibtex
|
| 226 |
+
@misc{e2am_ablation_effnetv2s,
|
| 227 |
+
title = {E2AM: Energy-Aware Adaptive Model Training Ablation Study (EfficientNetV2-S)},
|
| 228 |
+
author = {Shanmuk},
|
| 229 |
+
year = {2026},
|
| 230 |
+
howpublished = {\url{https://huggingface.co/datasets/Shanmuk4622/E2AM_EfficientNetV2_S}},
|
| 231 |
+
}
|
| 232 |
+
```
|
| 233 |
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| 234 |
+
---
|
| 235 |
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|
| 236 |
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_This README was auto-generated on 2026-06-06 12:40 UTC._
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_Source repo: Shanmuk4622/E2AM_EfficientNetV2_S_
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dataset,method_group,variant_name,epochs_run,best_top1,best_top5,status,final_f1,total_energy_j,total_energy_kwh,total_co2_kg,total_time_sec,peak_vram_mb,num_params,model_name,batch_size,amp_enabled
|
| 2 |
+
cifar10,cumulative_ablation,C0_baseline,50,0.7569,0.9865,completed,0.7567631050184673,715745.4979676766,0.19881819387991018,0.0944386420929573,9591.358461856842,4275.3671875,,effnetv2_s,64,False
|
| 3 |
+
cifar10,cumulative_ablation,C1_cache,50,0.7472,0.9862,completed,0.7151941497456357,714489.4625954495,0.19846929516540265,0.09427291520356623,9556.001816034317,4356.42626953125,,effnetv2_s,64,False
|
| 4 |
+
cifar10,cumulative_ablation,C2_cache_amp,50,0.8068,0.9913,completed,0.7805034047407651,355666.4200145934,0.0987962277818315,0.04692820819636996,4823.602003335953,4141.09326171875,,effnetv2_s,128,True
|
| 5 |
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cifar10,cumulative_ablation,C3_cache_amp_gradaccum,50,0.8345,0.9937,completed,0.826550211550648,355558.2829653582,0.0987661897125995,0.04691394011348475,4810.303344011307,3451.18017578125,,effnetv2_s,128,True
|
| 6 |
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cifar10,cumulative_ablation,C4_cache_amp_gradaccum_adaptivelr,50,0.8973,0.9971,completed,0.8968777580440346,356141.79610996257,0.09892827669721183,0.04699093143117559,4825.418663740158,3451.18017578125,,effnetv2_s,128,True
|
| 7 |
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cifar10,cumulative_ablation,C5_cache_amp_gradaccum_adaptivelr_l1,50,0.904,0.9977,completed,0.9007595150787328,362231.53360416065,0.10061987044560018,0.04779443846166009,4909.4348776340485,3517.29150390625,,effnetv2_s,128,True
|
| 8 |
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cifar10,cumulative_ablation,C6_full_e2am,50,0.904,0.9977,completed,0.9007595150787328,362560.41227393923,0.10071122563164979,0.04783783217503364,4914.6468341350555,3517.29150390625,,effnetv2_s,128,True
|
| 9 |
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cifar10,individual_methods,M0_baseline_fp32,50,0.7565,0.9839,completed,0.7104833801514389,748873.5835577279,0.20802043987714663,0.09880970894164465,9714.074770212173,3274.1494140625,,effnetv2_s,64,False
|
| 10 |
+
cifar10,individual_methods,M1_cache_only,50,0.7582,0.9851,completed,0.7211660263438614,706609.5648886206,0.1962804346912835,0.09323320647835964,9440.229766607285,3279.6494140625,,effnetv2_s,64,False
|
| 11 |
+
cifar10,individual_methods,M2_amp_only,50,0.8013,0.9909,completed,0.7825241449345911,355110.9907349747,0.0986419418708263,0.04685492238864247,4774.621376514435,4141.09326171875,,effnetv2_s,128,True
|
| 12 |
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cifar10,individual_methods,M3_grad_accum_only,50,0.8036,0.9903,completed,0.8046504166810449,703650.7162640394,0.1954585322955665,0.09284280284039406,9402.74931025505,3377.6591796875,,effnetv2_s,64,False
|
| 13 |
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cifar10,individual_methods,M4_l1_sparsity_only,50,0.7394,0.9837,completed,0.7115685968521982,755073.0795135935,0.2097425220871093,0.09962769799137693,9834.075927495956,4436.50146484375,,effnetv2_s,64,False
|
| 14 |
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cifar10,individual_methods,M5_adaptive_lr_only,50,0.8841,0.9969,completed,0.8835350810369855,706780.1941463734,0.19632783170732596,0.09325572006097982,9453.784047603607,4275.3671875,,effnetv2_s,64,False
|
| 15 |
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cifar10,individual_methods,M6_eag_only,50,0.759,0.9854,completed,0.7489919392263508,748944.6195198044,0.20804017208883455,0.0988190817421964,9713.186405181885,3274.1494140625,,effnetv2_s,64,False
|
| 16 |
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cifar10,individual_methods,M7_full_e2am,50,0.8974,0.9967,completed,0.8970660158012633,361860.22222248797,0.10051672839513555,0.04774544598768936,4846.892284154892,3517.29150390625,,effnetv2_s,128,True
|
| 17 |
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cifar100,cumulative_ablation,C0_baseline,50,0.4075,0.7359,completed,0.38017382495678226,719694.8510771834,0.1999152364103287,0.09495973729490613,9529.088878393173,4276.26806640625,,effnetv2_s,64,False
|
| 18 |
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cifar100,cumulative_ablation,C1_cache,50,0.4502,0.77,completed,0.4225739519111621,719342.1852594784,0.19981727368318844,0.0949132049995145,9533.11862373352,3280.48974609375,,effnetv2_s,64,False
|
| 19 |
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cifar100,cumulative_ablation,C2_cache_amp,50,0.5411,0.8401,completed,0.532219628149945,353351.09632191015,0.09815308231164172,0.04662271409802978,4755.724461078644,4220.99267578125,,effnetv2_s,128,True
|
| 20 |
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cifar100,cumulative_ablation,C3_cache_amp_gradaccum,50,0.6111,0.8775,completed,0.6077417417503063,343255.0681532182,0.09534863004256061,0.0452905992702163,4577.647082090378,3447.2705078125,,effnetv2_s,128,True
|
| 21 |
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cifar100,cumulative_ablation,C4_cache_amp_gradaccum_adaptivelr,50,0.7096,0.9251,completed,0.7095778563442227,343462.84592797264,0.09540634609110352,0.045318014393274146,4587.174750328064,3447.2705078125,,effnetv2_s,128,True
|
| 22 |
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cifar100,cumulative_ablation,C5_cache_amp_gradaccum_adaptivelr_l1,50,0.7016,0.9205,completed,0.7008570618811472,348584.5725117401,0.0968290479199278,0.045993797761965695,4671.625258922577,3514.6318359375,,effnetv2_s,128,True
|
| 23 |
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cifar100,cumulative_ablation,C6_full_e2am,50,0.7017,0.9191,completed,0.7008276431840714,359654.39714378526,0.09990399920660702,0.0474543996231383,4811.688164949417,4303.74560546875,,effnetv2_s,128,True
|
| 24 |
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cifar100,individual_methods,M0_baseline_fp32,50,0.39,0.7104,completed,0.348462858126309,716245.9146040115,0.1989571985011143,0.09450466928802921,9331.550752878189,4357.7666015625,,effnetv2_s,64,False
|
| 25 |
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cifar100,individual_methods,M1_cache_only,50,0.419,0.7465,completed,0.39069131493658227,717076.1133236968,0.19918780925658244,0.09461420939687662,9688.777062177658,3275.48974609375,,effnetv2_s,64,False
|
| 26 |
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cifar100,individual_methods,M2_amp_only,50,0.524,0.8238,completed,0.510377780408308,344278.61732102185,0.0956329492558394,0.045425650896523714,4607.844691991806,4141.994140625,,effnetv2_s,128,True
|
| 27 |
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|
| 28 |
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cifar100,individual_methods,M4_l1_sparsity_only,50,0.4378,0.7671,completed,0.40693517854586914,726744.9364938159,0.2018735934705044,0.09588995689848955,9629.383927822113,4437.841796875,,effnetv2_s,64,False
|
| 29 |
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cifar100,individual_methods,M5_adaptive_lr_only,50,0.5975,0.8641,completed,0.5931944356967066,718248.7766084878,0.1995135490579133,0.09476893580250875,9677.77730679512,4357.7666015625,,effnetv2_s,64,False
|
| 30 |
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cifar100,individual_methods,M6_eag_only,50,0.401,0.7281,completed,0.35379156609108425,719728.6732300554,0.19992463145279316,0.09496419994007675,9519.850924015045,4357.7666015625,,effnetv2_s,64,False
|
| 31 |
+
cifar100,individual_methods,M7_full_e2am,50,0.709,0.9254,completed,0.709077639234033,368820.7558532579,0.1024502099592383,0.04866384973063821,4969.657219171524,3511.8818359375,,effnetv2_s,128,True
|
| 32 |
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|
| 33 |
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tiny_imagenet,cumulative_ablation,C1_cache,50,0.2502,0.5186,completed,0.1949103022392003,1445139.206839809,0.4014275574555025,0.1906780897913636,19311.573588371277,4358.75732421875,,effnetv2_s,64,False
|
| 34 |
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tiny_imagenet,cumulative_ablation,C2_cache_amp,50,0.3465,0.63,completed,0.3187290301283518,712632.4652320796,0.19795346256446655,0.09402789471812158,9632.562858581543,4146.99609375,,effnetv2_s,128,True
|
| 35 |
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|
| 36 |
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tiny_imagenet,cumulative_ablation,C4_cache_amp_gradaccum_adaptivelr,50,0.5738,0.8162,completed,0.5686480650736732,729532.9497632972,0.2026480416009159,0.09625781976043499,9831.056418895721,4228.9833984375,,effnetv2_s,128,True
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tiny_imagenet,cumulative_ablation,C6_full_e2am,50,0.5708,0.8125,completed,0.5653354983135468,738944.0320090081,0.20526223111361336,0.09749955977896638,9939.098759174347,3518.37255859375,,effnetv2_s,128,True
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tiny_imagenet,individual_methods,M0_baseline_fp32,50,0.2462,0.517,completed,0.22385366809432974,1422660.698390391,0.39518352733066414,0.18771217548206537,18948.88562297821,4358.75732421875,,effnetv2_s,64,False
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tiny_imagenet,individual_methods,M1_cache_only,50,0.2451,0.5128,completed,0.1829452299811285,1419903.9479109242,0.3944177633085901,0.18734843757158012,19007.647489786148,4358.75732421875,,effnetv2_s,64,False
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|
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tiny_imagenet,individual_methods,M4_l1_sparsity_only,50,0.244,0.5172,completed,0.21034926513662713,1459562.137277583,0.4054339270215508,0.19258111533523645,19502.241866350174,4440.33251953125,,effnetv2_s,64,False
|
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tiny_imagenet,individual_methods,M5_adaptive_lr_only,50,0.4611,0.7419,completed,0.449640824435746,1432503.4575608147,0.3979176271002263,0.18901087287260743,19208.21226501465,4358.75732421875,,effnetv2_s,64,False
|
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tiny_imagenet,individual_methods,M6_eag_only,50,0.2606,0.5416,completed,0.2092075670152726,1437299.678006703,0.3992499105574175,0.1896437075147733,19318.716631412506,3285.48046875,,effnetv2_s,64,False
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tiny_imagenet,individual_methods,M7_full_e2am,50,0.5805,0.8162,completed,0.575147313173946,721653.1458821703,0.20045920718949176,0.09521812341500858,9682.665334701538,3519.12255859375,,effnetv2_s,128,True
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dataset,method_group,variant_name,epochs_run,best_top1,best_top5,status,final_f1,total_energy_j,total_energy_kwh,total_co2_kg,total_time_sec,peak_vram_mb,num_params,model_name,batch_size,amp_enabled
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+
cifar10,cumulative_ablation,C0_baseline,50,0.7569,0.9865,completed,0.7567631050184673,715745.4979676766,0.19881819387991018,0.0944386420929573,9591.358461856842,4275.3671875,,effnetv2_s,64,False
|
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+
cifar10,cumulative_ablation,C1_cache,50,0.7472,0.9862,completed,0.7151941497456357,714489.4625954495,0.19846929516540265,0.09427291520356623,9556.001816034317,4356.42626953125,,effnetv2_s,64,False
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+
cifar10,cumulative_ablation,C2_cache_amp,50,0.8068,0.9913,completed,0.7805034047407651,355666.4200145934,0.0987962277818315,0.04692820819636996,4823.602003335953,4141.09326171875,,effnetv2_s,128,True
|
| 5 |
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cifar10,cumulative_ablation,C3_cache_amp_gradaccum,50,0.8345,0.9937,completed,0.826550211550648,355558.2829653582,0.0987661897125995,0.04691394011348475,4810.303344011307,3451.18017578125,,effnetv2_s,128,True
|
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+
cifar10,cumulative_ablation,C4_cache_amp_gradaccum_adaptivelr,50,0.8973,0.9971,completed,0.8968777580440346,356141.79610996257,0.09892827669721183,0.04699093143117559,4825.418663740158,3451.18017578125,,effnetv2_s,128,True
|
| 7 |
+
cifar10,cumulative_ablation,C5_cache_amp_gradaccum_adaptivelr_l1,50,0.904,0.9977,completed,0.9007595150787328,362231.53360416065,0.10061987044560018,0.04779443846166009,4909.4348776340485,3517.29150390625,,effnetv2_s,128,True
|
| 8 |
+
cifar10,cumulative_ablation,C6_full_e2am,50,0.904,0.9977,completed,0.9007595150787328,362560.41227393923,0.10071122563164979,0.04783783217503364,4914.6468341350555,3517.29150390625,,effnetv2_s,128,True
|
| 9 |
+
cifar100,cumulative_ablation,C0_baseline,50,0.4075,0.7359,completed,0.38017382495678226,719694.8510771834,0.1999152364103287,0.09495973729490613,9529.088878393173,4276.26806640625,,effnetv2_s,64,False
|
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|
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|
| 12 |
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|
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|
| 14 |
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cifar100,cumulative_ablation,C5_cache_amp_gradaccum_adaptivelr_l1,50,0.7016,0.9205,completed,0.7008570618811472,348584.5725117401,0.0968290479199278,0.045993797761965695,4671.625258922577,3514.6318359375,,effnetv2_s,128,True
|
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cifar100,cumulative_ablation,C6_full_e2am,50,0.7017,0.9191,completed,0.7008276431840714,359654.39714378526,0.09990399920660702,0.0474543996231383,4811.688164949417,4303.74560546875,,effnetv2_s,128,True
|
| 16 |
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tiny_imagenet,cumulative_ablation,C0_baseline,50,0.2475,0.5199,completed,0.21126754685035634,1482909.3487123142,0.4119192635311984,0.19566165017731924,19800.469193696976,4358.75732421875,,effnetv2_s,64,False
|
| 17 |
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tiny_imagenet,cumulative_ablation,C1_cache,50,0.2502,0.5186,completed,0.1949103022392003,1445139.206839809,0.4014275574555025,0.1906780897913636,19311.573588371277,4358.75732421875,,effnetv2_s,64,False
|
| 18 |
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tiny_imagenet,cumulative_ablation,C2_cache_amp,50,0.3465,0.63,completed,0.3187290301283518,712632.4652320796,0.19795346256446655,0.09402789471812158,9632.562858581543,4146.99609375,,effnetv2_s,128,True
|
| 19 |
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tiny_imagenet,cumulative_ablation,C3_cache_amp_gradaccum,50,0.4436,0.7242,completed,0.439476251604306,724321.9327641955,0.20120053687894318,0.09557025501749802,9765.76681804657,3453.76123046875,,effnetv2_s,128,True
|
| 20 |
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tiny_imagenet,cumulative_ablation,C4_cache_amp_gradaccum_adaptivelr,50,0.5738,0.8162,completed,0.5686480650736732,729532.9497632972,0.2026480416009159,0.09625781976043499,9831.056418895721,4228.9833984375,,effnetv2_s,128,True
|
| 21 |
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tiny_imagenet,cumulative_ablation,C5_cache_amp_gradaccum_adaptivelr_l1,50,0.5708,0.8125,completed,0.5653354983135468,739216.8172919098,0.2053380048033083,0.09753555228157143,9943.097280025482,3518.37255859375,,effnetv2_s,128,True
|
| 22 |
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tiny_imagenet,cumulative_ablation,C6_full_e2am,50,0.5708,0.8125,completed,0.5653354983135468,738944.0320090081,0.20526223111361336,0.09749955977896638,9939.098759174347,3518.37255859375,,effnetv2_s,128,True
|
paper_tables/equal_epoch_comparison.csv
CHANGED
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@@ -1,5 +1,46 @@
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| 1 |
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dataset,method_group,variant_name,epochs_run,best_top1,best_top5,status,final_f1,total_energy_j,total_energy_kwh,total_co2_kg,total_time_sec,peak_vram_mb,num_params,model_name,batch_size,amp_enabled
|
| 2 |
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cifar10,cumulative_ablation,C0_baseline,50,0.7569,0.9865,completed,0.7567631050184673,715745.4979676766,0.19881819387991018,0.0944386420929573,9591.358461856842,4275.3671875,,effnetv2_s,64,False
|
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cifar10,cumulative_ablation,C1_cache,50,0.7472,0.9862,completed,0.7151941497456357,714489.4625954495,0.19846929516540265,0.09427291520356623,9556.001816034317,4356.42626953125,,effnetv2_s,64,False
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|
| 9 |
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cifar10,individual_methods,M0_baseline_fp32,50,0.7565,0.9839,completed,0.7104833801514389,748873.5835577279,0.20802043987714663,0.09880970894164465,9714.074770212173,3274.1494140625,,effnetv2_s,64,False
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| 10 |
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|
| 11 |
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cifar10,individual_methods,M2_amp_only,50,0.8013,0.9909,completed,0.7825241449345911,355110.9907349747,0.0986419418708263,0.04685492238864247,4774.621376514435,4141.09326171875,,effnetv2_s,128,True
|
| 12 |
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cifar10,individual_methods,M3_grad_accum_only,50,0.8036,0.9903,completed,0.8046504166810449,703650.7162640394,0.1954585322955665,0.09284280284039406,9402.74931025505,3377.6591796875,,effnetv2_s,64,False
|
| 13 |
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cifar10,individual_methods,M4_l1_sparsity_only,50,0.7394,0.9837,completed,0.7115685968521982,755073.0795135935,0.2097425220871093,0.09962769799137693,9834.075927495956,4436.50146484375,,effnetv2_s,64,False
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| 16 |
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|
| 18 |
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| 24 |
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| 25 |
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| 27 |
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| 28 |
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cifar100,individual_methods,M4_l1_sparsity_only,50,0.4378,0.7671,completed,0.40693517854586914,726744.9364938159,0.2018735934705044,0.09588995689848955,9629.383927822113,4437.841796875,,effnetv2_s,64,False
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| 29 |
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| 30 |
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cifar100,individual_methods,M6_eag_only,50,0.401,0.7281,completed,0.35379156609108425,719728.6732300554,0.19992463145279316,0.09496419994007675,9519.850924015045,4357.7666015625,,effnetv2_s,64,False
|
| 31 |
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cifar100,individual_methods,M7_full_e2am,50,0.709,0.9254,completed,0.709077639234033,368820.7558532579,0.1024502099592383,0.04866384973063821,4969.657219171524,3511.8818359375,,effnetv2_s,128,True
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| 32 |
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tiny_imagenet,cumulative_ablation,C0_baseline,50,0.2475,0.5199,completed,0.21126754685035634,1482909.3487123142,0.4119192635311984,0.19566165017731924,19800.469193696976,4358.75732421875,,effnetv2_s,64,False
|
| 33 |
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tiny_imagenet,cumulative_ablation,C1_cache,50,0.2502,0.5186,completed,0.1949103022392003,1445139.206839809,0.4014275574555025,0.1906780897913636,19311.573588371277,4358.75732421875,,effnetv2_s,64,False
|
| 34 |
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| 35 |
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| 36 |
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| 37 |
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|
| 38 |
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tiny_imagenet,cumulative_ablation,C6_full_e2am,50,0.5708,0.8125,completed,0.5653354983135468,738944.0320090081,0.20526223111361336,0.09749955977896638,9939.098759174347,3518.37255859375,,effnetv2_s,128,True
|
| 39 |
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|
| 40 |
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|
| 41 |
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|
| 42 |
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|
| 43 |
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|
| 44 |
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| 45 |
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| 46 |
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|
paper_tables/individual_method_study.csv
CHANGED
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dataset,method_group,variant_name,epochs_run,best_top1,best_top5,status,final_f1,total_energy_j,total_energy_kwh,total_co2_kg,total_time_sec,peak_vram_mb,num_params,model_name,batch_size,amp_enabled
|
| 2 |
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cifar10,individual_methods,M0_baseline_fp32,50,0.7565,0.9839,completed,0.7104833801514389,748873.5835577279,0.20802043987714663,0.09880970894164465,9714.074770212173,3274.1494140625,,effnetv2_s,64,False
|
| 3 |
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|
| 4 |
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|
| 5 |
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|
| 6 |
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|
| 7 |
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cifar10,individual_methods,M5_adaptive_lr_only,50,0.8841,0.9969,completed,0.8835350810369855,706780.1941463734,0.19632783170732596,0.09325572006097982,9453.784047603607,4275.3671875,,effnetv2_s,64,False
|
| 8 |
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cifar10,individual_methods,M6_eag_only,50,0.759,0.9854,completed,0.7489919392263508,748944.6195198044,0.20804017208883455,0.0988190817421964,9713.186405181885,3274.1494140625,,effnetv2_s,64,False
|
| 9 |
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cifar10,individual_methods,M7_full_e2am,50,0.8974,0.9967,completed,0.8970660158012633,361860.22222248797,0.10051672839513555,0.04774544598768936,4846.892284154892,3517.29150390625,,effnetv2_s,128,True
|
| 10 |
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cifar100,individual_methods,M0_baseline_fp32,50,0.39,0.7104,completed,0.348462858126309,716245.9146040115,0.1989571985011143,0.09450466928802921,9331.550752878189,4357.7666015625,,effnetv2_s,64,False
|
| 11 |
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cifar100,individual_methods,M1_cache_only,50,0.419,0.7465,completed,0.39069131493658227,717076.1133236968,0.19918780925658244,0.09461420939687662,9688.777062177658,3275.48974609375,,effnetv2_s,64,False
|
| 12 |
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cifar100,individual_methods,M2_amp_only,50,0.524,0.8238,completed,0.510377780408308,344278.61732102185,0.0956329492558394,0.045425650896523714,4607.844691991806,4141.994140625,,effnetv2_s,128,True
|
| 13 |
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cifar100,individual_methods,M3_grad_accum_only,50,0.5408,0.8365,completed,0.5297944322806143,714388.122358892,0.1984411450996922,0.09425954392235378,9629.036025047302,4357.7666015625,,effnetv2_s,64,False
|
| 14 |
+
cifar100,individual_methods,M4_l1_sparsity_only,50,0.4378,0.7671,completed,0.40693517854586914,726744.9364938159,0.2018735934705044,0.09588995689848955,9629.383927822113,4437.841796875,,effnetv2_s,64,False
|
| 15 |
+
cifar100,individual_methods,M5_adaptive_lr_only,50,0.5975,0.8641,completed,0.5931944356967066,718248.7766084878,0.1995135490579133,0.09476893580250875,9677.77730679512,4357.7666015625,,effnetv2_s,64,False
|
| 16 |
+
cifar100,individual_methods,M6_eag_only,50,0.401,0.7281,completed,0.35379156609108425,719728.6732300554,0.19992463145279316,0.09496419994007675,9519.850924015045,4357.7666015625,,effnetv2_s,64,False
|
| 17 |
+
cifar100,individual_methods,M7_full_e2am,50,0.709,0.9254,completed,0.709077639234033,368820.7558532579,0.1024502099592383,0.04866384973063821,4969.657219171524,3511.8818359375,,effnetv2_s,128,True
|
| 18 |
+
tiny_imagenet,individual_methods,M0_baseline_fp32,50,0.2462,0.517,completed,0.22385366809432974,1422660.698390391,0.39518352733066414,0.18771217548206537,18948.88562297821,4358.75732421875,,effnetv2_s,64,False
|
| 19 |
+
tiny_imagenet,individual_methods,M1_cache_only,50,0.2451,0.5128,completed,0.1829452299811285,1419903.9479109242,0.3944177633085901,0.18734843757158012,19007.647489786148,4358.75732421875,,effnetv2_s,64,False
|
| 20 |
+
tiny_imagenet,individual_methods,M2_amp_only,50,0.3541,0.6305,completed,0.29404092167122764,697223.2055240303,0.19367311264556397,0.09199472850664284,9387.331252336502,4229.9833984375,,effnetv2_s,128,True
|
| 21 |
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tiny_imagenet,individual_methods,M3_grad_accum_only,50,0.3467,0.6401,completed,0.32429801105572165,1411645.6268399907,0.39212378523333075,0.18625879798583214,18791.001306295395,3383.490234375,,effnetv2_s,64,False
|
| 22 |
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tiny_imagenet,individual_methods,M4_l1_sparsity_only,50,0.244,0.5172,completed,0.21034926513662713,1459562.137277583,0.4054339270215508,0.19258111533523645,19502.241866350174,4440.33251953125,,effnetv2_s,64,False
|
| 23 |
+
tiny_imagenet,individual_methods,M5_adaptive_lr_only,50,0.4611,0.7419,completed,0.449640824435746,1432503.4575608147,0.3979176271002263,0.18901087287260743,19208.21226501465,4358.75732421875,,effnetv2_s,64,False
|
| 24 |
+
tiny_imagenet,individual_methods,M6_eag_only,50,0.2606,0.5416,completed,0.2092075670152726,1437299.678006703,0.3992499105574175,0.1896437075147733,19318.716631412506,3285.48046875,,effnetv2_s,64,False
|
| 25 |
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tiny_imagenet,individual_methods,M7_full_e2am,50,0.5805,0.8162,completed,0.575147313173946,721653.1458821703,0.20045920718949176,0.09521812341500858,9682.665334701538,3519.12255859375,,effnetv2_s,128,True
|
paper_tables/training_results_table.csv
CHANGED
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| 1 |
+
dataset,method_group,variant_name,model_name,batch_size,amp_enabled,epochs_run,best_top1,best_top5,final_f1,total_energy_kwh,total_co2_kg,total_time_sec,peak_vram_mb,status
|
| 2 |
+
cifar10,cumulative_ablation,C0_baseline,effnetv2_s,64,False,50,0.7569,0.9865,0.7567631050184673,0.19881819387991018,0.0944386420929573,9591.358461856842,4275.3671875,completed
|
| 3 |
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cifar10,cumulative_ablation,C1_cache,effnetv2_s,64,False,50,0.7472,0.9862,0.7151941497456357,0.19846929516540265,0.09427291520356623,9556.001816034317,4356.42626953125,completed
|
| 4 |
+
cifar10,cumulative_ablation,C2_cache_amp,effnetv2_s,128,True,50,0.8068,0.9913,0.7805034047407651,0.0987962277818315,0.04692820819636996,4823.602003335953,4141.09326171875,completed
|
| 5 |
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cifar10,cumulative_ablation,C3_cache_amp_gradaccum,effnetv2_s,128,True,50,0.8345,0.9937,0.826550211550648,0.0987661897125995,0.04691394011348475,4810.303344011307,3451.18017578125,completed
|
| 6 |
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cifar10,cumulative_ablation,C4_cache_amp_gradaccum_adaptivelr,effnetv2_s,128,True,50,0.8973,0.9971,0.8968777580440346,0.09892827669721183,0.04699093143117559,4825.418663740158,3451.18017578125,completed
|
| 7 |
+
cifar10,cumulative_ablation,C5_cache_amp_gradaccum_adaptivelr_l1,effnetv2_s,128,True,50,0.904,0.9977,0.9007595150787328,0.10061987044560018,0.04779443846166009,4909.4348776340485,3517.29150390625,completed
|
| 8 |
+
cifar10,cumulative_ablation,C6_full_e2am,effnetv2_s,128,True,50,0.904,0.9977,0.9007595150787328,0.10071122563164979,0.04783783217503364,4914.6468341350555,3517.29150390625,completed
|
| 9 |
+
cifar10,individual_methods,M0_baseline_fp32,effnetv2_s,64,False,50,0.7565,0.9839,0.7104833801514389,0.20802043987714663,0.09880970894164465,9714.074770212173,3274.1494140625,completed
|
| 10 |
+
cifar10,individual_methods,M1_cache_only,effnetv2_s,64,False,50,0.7582,0.9851,0.7211660263438614,0.1962804346912835,0.09323320647835964,9440.229766607285,3279.6494140625,completed
|
| 11 |
+
cifar10,individual_methods,M2_amp_only,effnetv2_s,128,True,50,0.8013,0.9909,0.7825241449345911,0.0986419418708263,0.04685492238864247,4774.621376514435,4141.09326171875,completed
|
| 12 |
+
cifar10,individual_methods,M3_grad_accum_only,effnetv2_s,64,False,50,0.8036,0.9903,0.8046504166810449,0.1954585322955665,0.09284280284039406,9402.74931025505,3377.6591796875,completed
|
| 13 |
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cifar10,individual_methods,M4_l1_sparsity_only,effnetv2_s,64,False,50,0.7394,0.9837,0.7115685968521982,0.2097425220871093,0.09962769799137693,9834.075927495956,4436.50146484375,completed
|
| 14 |
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cifar10,individual_methods,M5_adaptive_lr_only,effnetv2_s,64,False,50,0.8841,0.9969,0.8835350810369855,0.19632783170732596,0.09325572006097982,9453.784047603607,4275.3671875,completed
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| 15 |
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cifar10,individual_methods,M6_eag_only,effnetv2_s,64,False,50,0.759,0.9854,0.7489919392263508,0.20804017208883455,0.0988190817421964,9713.186405181885,3274.1494140625,completed
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| 16 |
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cifar10,individual_methods,M7_full_e2am,effnetv2_s,128,True,50,0.8974,0.9967,0.8970660158012633,0.10051672839513555,0.04774544598768936,4846.892284154892,3517.29150390625,completed
|
| 17 |
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cifar100,cumulative_ablation,C0_baseline,effnetv2_s,64,False,50,0.4075,0.7359,0.38017382495678226,0.1999152364103287,0.09495973729490613,9529.088878393173,4276.26806640625,completed
|
| 18 |
+
cifar100,cumulative_ablation,C1_cache,effnetv2_s,64,False,50,0.4502,0.77,0.4225739519111621,0.19981727368318844,0.0949132049995145,9533.11862373352,3280.48974609375,completed
|
| 19 |
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cifar100,cumulative_ablation,C2_cache_amp,effnetv2_s,128,True,50,0.5411,0.8401,0.532219628149945,0.09815308231164172,0.04662271409802978,4755.724461078644,4220.99267578125,completed
|
| 20 |
+
cifar100,cumulative_ablation,C3_cache_amp_gradaccum,effnetv2_s,128,True,50,0.6111,0.8775,0.6077417417503063,0.09534863004256061,0.0452905992702163,4577.647082090378,3447.2705078125,completed
|
| 21 |
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cifar100,cumulative_ablation,C4_cache_amp_gradaccum_adaptivelr,effnetv2_s,128,True,50,0.7096,0.9251,0.7095778563442227,0.09540634609110352,0.045318014393274146,4587.174750328064,3447.2705078125,completed
|
| 22 |
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cifar100,cumulative_ablation,C5_cache_amp_gradaccum_adaptivelr_l1,effnetv2_s,128,True,50,0.7016,0.9205,0.7008570618811472,0.0968290479199278,0.045993797761965695,4671.625258922577,3514.6318359375,completed
|
| 23 |
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cifar100,cumulative_ablation,C6_full_e2am,effnetv2_s,128,True,50,0.7017,0.9191,0.7008276431840714,0.09990399920660702,0.0474543996231383,4811.688164949417,4303.74560546875,completed
|
| 24 |
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cifar100,individual_methods,M0_baseline_fp32,effnetv2_s,64,False,50,0.39,0.7104,0.348462858126309,0.1989571985011143,0.09450466928802921,9331.550752878189,4357.7666015625,completed
|
| 25 |
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cifar100,individual_methods,M1_cache_only,effnetv2_s,64,False,50,0.419,0.7465,0.39069131493658227,0.19918780925658244,0.09461420939687662,9688.777062177658,3275.48974609375,completed
|
| 26 |
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cifar100,individual_methods,M2_amp_only,effnetv2_s,128,True,50,0.524,0.8238,0.510377780408308,0.0956329492558394,0.045425650896523714,4607.844691991806,4141.994140625,completed
|
| 27 |
+
cifar100,individual_methods,M3_grad_accum_only,effnetv2_s,64,False,50,0.5408,0.8365,0.5297944322806143,0.1984411450996922,0.09425954392235378,9629.036025047302,4357.7666015625,completed
|
| 28 |
+
cifar100,individual_methods,M4_l1_sparsity_only,effnetv2_s,64,False,50,0.4378,0.7671,0.40693517854586914,0.2018735934705044,0.09588995689848955,9629.383927822113,4437.841796875,completed
|
| 29 |
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cifar100,individual_methods,M5_adaptive_lr_only,effnetv2_s,64,False,50,0.5975,0.8641,0.5931944356967066,0.1995135490579133,0.09476893580250875,9677.77730679512,4357.7666015625,completed
|
| 30 |
+
cifar100,individual_methods,M6_eag_only,effnetv2_s,64,False,50,0.401,0.7281,0.35379156609108425,0.19992463145279316,0.09496419994007675,9519.850924015045,4357.7666015625,completed
|
| 31 |
+
cifar100,individual_methods,M7_full_e2am,effnetv2_s,128,True,50,0.709,0.9254,0.709077639234033,0.1024502099592383,0.04866384973063821,4969.657219171524,3511.8818359375,completed
|
| 32 |
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tiny_imagenet,cumulative_ablation,C0_baseline,effnetv2_s,64,False,50,0.2475,0.5199,0.21126754685035634,0.4119192635311984,0.19566165017731924,19800.469193696976,4358.75732421875,completed
|
| 33 |
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tiny_imagenet,cumulative_ablation,C1_cache,effnetv2_s,64,False,50,0.2502,0.5186,0.1949103022392003,0.4014275574555025,0.1906780897913636,19311.573588371277,4358.75732421875,completed
|
| 34 |
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tiny_imagenet,cumulative_ablation,C2_cache_amp,effnetv2_s,128,True,50,0.3465,0.63,0.3187290301283518,0.19795346256446655,0.09402789471812158,9632.562858581543,4146.99609375,completed
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| 35 |
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tiny_imagenet,cumulative_ablation,C3_cache_amp_gradaccum,effnetv2_s,128,True,50,0.4436,0.7242,0.439476251604306,0.20120053687894318,0.09557025501749802,9765.76681804657,3453.76123046875,completed
|
| 36 |
+
tiny_imagenet,cumulative_ablation,C4_cache_amp_gradaccum_adaptivelr,effnetv2_s,128,True,50,0.5738,0.8162,0.5686480650736732,0.2026480416009159,0.09625781976043499,9831.056418895721,4228.9833984375,completed
|
| 37 |
+
tiny_imagenet,cumulative_ablation,C5_cache_amp_gradaccum_adaptivelr_l1,effnetv2_s,128,True,50,0.5708,0.8125,0.5653354983135468,0.2053380048033083,0.09753555228157143,9943.097280025482,3518.37255859375,completed
|
| 38 |
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tiny_imagenet,cumulative_ablation,C6_full_e2am,effnetv2_s,128,True,50,0.5708,0.8125,0.5653354983135468,0.20526223111361336,0.09749955977896638,9939.098759174347,3518.37255859375,completed
|
| 39 |
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tiny_imagenet,individual_methods,M0_baseline_fp32,effnetv2_s,64,False,50,0.2462,0.517,0.22385366809432974,0.39518352733066414,0.18771217548206537,18948.88562297821,4358.75732421875,completed
|
| 40 |
+
tiny_imagenet,individual_methods,M1_cache_only,effnetv2_s,64,False,50,0.2451,0.5128,0.1829452299811285,0.3944177633085901,0.18734843757158012,19007.647489786148,4358.75732421875,completed
|
| 41 |
+
tiny_imagenet,individual_methods,M2_amp_only,effnetv2_s,128,True,50,0.3541,0.6305,0.29404092167122764,0.19367311264556397,0.09199472850664284,9387.331252336502,4229.9833984375,completed
|
| 42 |
+
tiny_imagenet,individual_methods,M3_grad_accum_only,effnetv2_s,64,False,50,0.3467,0.6401,0.32429801105572165,0.39212378523333075,0.18625879798583214,18791.001306295395,3383.490234375,completed
|
| 43 |
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tiny_imagenet,individual_methods,M4_l1_sparsity_only,effnetv2_s,64,False,50,0.244,0.5172,0.21034926513662713,0.4054339270215508,0.19258111533523645,19502.241866350174,4440.33251953125,completed
|
| 44 |
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tiny_imagenet,individual_methods,M5_adaptive_lr_only,effnetv2_s,64,False,50,0.4611,0.7419,0.449640824435746,0.3979176271002263,0.18901087287260743,19208.21226501465,4358.75732421875,completed
|
| 45 |
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tiny_imagenet,individual_methods,M6_eag_only,effnetv2_s,64,False,50,0.2606,0.5416,0.2092075670152726,0.3992499105574175,0.1896437075147733,19318.716631412506,3285.48046875,completed
|
| 46 |
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tiny_imagenet,individual_methods,M7_full_e2am,effnetv2_s,128,True,50,0.5805,0.8162,0.575147313173946,0.20045920718949176,0.09521812341500858,9682.665334701538,3519.12255859375,completed
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