efficient-net
efficientnet-v2-l-adv-dataset
Perturb Adversarial Images
Verified adversarial examples for efficientnet_v2_l (torchvision/EfficientNet_V2_L_Weights.IMAGENET1K_V1), produced by the
Perturb network. Each row is one clean image together with all of its
verified adversarial versions: images that are imperceptibly different from the original
(L∞ ≤ 0.03 in [0,1] pixel scale) yet change the model's top-1 prediction.
This dataset grows continuously. New rows are appended as the network produces them and uploaded in… See the full description on the dataset page: https://huggingface.co/datasets/perturb-ai/efficientnet-v2-l-adv-dataset.neuralatlas-attributions-efficientnet_b0
Neural Atlas attributions — efficientnet_b0 on imagenet-pico
Precomputed attribution maps and faithfulness metrics for the torchvision
efficientnet_b0 model (default pretrained weights, no fine-tuning) on imagenet-pico,
a 3000-image subset of ImageNet-1k with three images for each of the 1000
classes.
This repository is part of Neural Atlas, a web tool for comparing
attribution methods across vision architectures on the same image, developed
as an undergraduate thesis at the… See the full description on the dataset page: https://huggingface.co/datasets/Matgc04/neuralatlas-attributions-efficientnet_b0.neuralatlas-attributions-efficientnet_b4
Neural Atlas attributions — efficientnet_b4 on imagenet-pico
Precomputed attribution maps and faithfulness metrics for the torchvision
efficientnet_b4 model (default pretrained weights, no fine-tuning) on imagenet-pico,
a 3000-image subset of ImageNet-1k with three images for each of the 1000
classes.
This repository is part of Neural Atlas, a web tool for comparing
attribution methods across vision architectures on the same image, developed
as an undergraduate thesis at the… See the full description on the dataset page: https://huggingface.co/datasets/Matgc04/neuralatlas-attributions-efficientnet_b4.E2AM_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… See the full description on the dataset page: https://huggingface.co/datasets/Shanmuk4622/E2AM_EfficientNetV2_S.efficientnet-food-datasetefficientnet_b0_b7_comparison_4000_rows