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The main features of xternalz/sdpoint are: Model Implementations, Convolutional Neural Networks (CNNs).
Projects with overlapping indexed features include: jwyang/faster-rcnn.pytorch — This project is a PyTorch object detection framework that implements the Faster R-CNN architecture. It serves as a… oeway/pytorch-deform-conv — PyTorch implementation of Deformable Convolution. 1zb/deformable-convolution-pytorch — PyTorch implementation of Deformable Convolution. bamos/densenet.pytorch — A PyTorch implementation of DenseNet. aaron-xichen/pytorch-playground — Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet,… rwightman/pytorch-image-models — This project is a library of pretrained computer vision architectures and backbones for image classification and…
Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)
PyTorch implementation of Deformable Convolution
This project is a PyTorch object detection framework that implements the Faster R-CNN architecture. It serves as a vision model for predicting precise bounding boxes around multiple objects within images and live video feeds. The system is optimized for multi-GPU training to reduce the time required for model convergence. It utilizes a GPU-accelerated design to handle the training and inference of complex detection networks. The framework covers the full object detection lifecycle, including custom network training and inference for static images and real-time video streams. It includes capa
