datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.