Resnet 152的权重文件。

简介：
The training of deeper neural networks presents significant challenges. We introduce a residual learning framework designed to simplify the training process for networks that are considerably deeper than those previously employed. Specifically, we reframe the network layers as functions that learn residual information relative to the layers inputs, rather than learning functions without such references. Our research furnishes extensive experimental data demonstrating that these residual networks are demonstrably simpler to optimize and can achieve enhanced accuracy through substantially increased depth. Evaluating residual networks reaching a depth of up to 152 layers – representing eight times the depth of VGG networks while maintaining reduced complexity – on the ImageNet dataset yielded an ensemble error rate of 3.57% on the test set. This achievement secured first place in the ILSVRC 2015 classification competition. Furthermore, we conducted analyses utilizing CIFAR-10 with both 100 and 1000 layers. The depth of learned representations plays a crucial role in numerous visual recognition tasks. As a direct consequence of our exceptionally deep representations, we realized a relative improvement of 28% on the COCO object detection dataset. Deep residual networks form the core of our contributions to the ILSVRC & COCO 2015 competitions, where we also attained first place in ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation tasks.