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01Matgc04 /neuralatlas-attributions-efficientnet_b4image0 likes679 downloads2d agoHugging Face02Matgc04 /neuralatlas-attributions-efficientnet_b0image0 likes347 downloads6h agoHugging Face03Shanmuk4622 /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.imagen<1K0 likes53 downloads4mo agoHugging Face04maia2000 /efficientnet-food-datasetimagen<1K0 likes18 downloads5mo agoHugging Face05AI-555 /EfficientNetB0_Folds_5splitsimagen<1K0 likes3 downloads5mo agoHugging Face06perturb-ai /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.imageimage-classificationn<1K0 likes14m agoHugging Face

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