ResNet-50
A 50-layer-deep residual neural network proposed by Microsoft Research. It uses a bottleneck structure to reduce computation and is one of the most commonly used benchmark models in image classification.
Timeline (last 90 days)
With a ResNet-50 backbone, the FDSML-pretrained model improved Recall@1 on the Stanford Online Products dataset from 65.6% to 74.0%.
ResNet-50 and MobileNetV3 are both commonly used ImageNet-pretrained backbone networks; the former has high accuracy, while the latter is suited to edge deployment.
BrainAid's performance metrics surpass those of standard models such as VGG16, Inception-V3 and ResNet-50.
Standard models such as VGG16, Inception-V3 and ResNet-50 have large parameter counts and high inference costs, and usually require GPU server support.
The paper tested GCD on CIFAR-10 and ImageNet, covering three architectures, ResNet-18, ResNet-50, and ViT-B/16, and evaluated four mainstream attack methods.
ResNet-50 has 25 million parameters and 4 GFLOPs of computation.
BlurPool can improve ResNet-50's shift consistency from approximately 89% to approximately 95%, while also yielding a small improvement in classification accuracy of about 0.5%.
MobileNetV3 Large 版本在 ImageNet 上达到 75.2% Top-1 精度，推理延迟仅为 ResNet-50 的约 1/5
ResNet-50 包含约 2560 万参数，在 ImageNet 上达到 76.1% 的 Top-1 精度
ResNet-50 在 8 张 NVIDIA V100 GPU 上训练 90 个 epoch 大约需要 29 小时，单张 V100 则需要约一周时间
All Facts (10)
With a ResNet-50 backbone, the FDSML-pretrained model improved Recall@1 on the Stanford Online Products dataset from 65.6% to 74.0%.
50%UnverifiedResNet-50 and MobileNetV3 are both commonly used ImageNet-pretrained backbone networks; the former has high accuracy, while the latter is suited to edge deployment.
50%UnverifiedBrainAid's performance metrics surpass those of standard models such as VGG16, Inception-V3 and ResNet-50.
50%UnverifiedStandard models such as VGG16, Inception-V3 and ResNet-50 have large parameter counts and high inference costs, and usually require GPU server support.
50%UnverifiedThe paper tested GCD on CIFAR-10 and ImageNet, covering three architectures, ResNet-18, ResNet-50, and ViT-B/16, and evaluated four mainstream attack methods.
50%UnverifiedResNet-50 has 25 million parameters and 4 GFLOPs of computation.
50%UnverifiedBlurPool can improve ResNet-50's shift consistency from approximately 89% to approximately 95%, while also yielding a small improvement in classification accuracy of about 0.5%.
50%UnverifiedMobileNetV3 Large 版本在 ImageNet 上达到 75.2% Top-1 精度，推理延迟仅为 ResNet-50 的约 1/5
50%UnverifiedResNet-50 包含约 2560 万参数，在 ImageNet 上达到 76.1% 的 Top-1 精度
50%UnverifiedResNet-50 在 8 张 NVIDIA V100 GPU 上训练 90 个 epoch 大约需要 29 小时，单张 V100 则需要约一周时间
50%