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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
Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)
PyTorch implementation of Deformable Convolution
PyTorch implementation of Deformable Convolution
The main features of 1zb/deformable-convolution-pytorch are: Computer Vision Models, Model Implementations, Convolutional Neural Networks (CNNs).
Projects with overlapping indexed features include: rwightman/pytorch-image-models — This project is a library of pretrained computer vision architectures and backbones for image classification and… bamos/densenet.pytorch — A PyTorch implementation of DenseNet. 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. aaron-xichen/pytorch-playground — Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet,… szagoruyko/attention-transfer — Improving Convolutional Networks via Attention Transfer (ICLR 2017).
