How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
PyTorch implementation of Deformable Convolution
Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)
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
PyTorch implementation of Deformable Convolution
The main features of oeway/pytorch-deform-conv are: Computer Vision Models, Model Implementations, Convolutional Neural Networks (CNNs).
Open-source alternatives to oeway/pytorch-deform-conv include: bamos/densenet.pytorch — A PyTorch implementation of DenseNet. rwightman/pytorch-image-models — This project is a library of pretrained computer vision architectures and backbones for image classification and… aaron-xichen/pytorch-playground — Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet,… 1zb/deformable-convolution-pytorch — PyTorch implementation of Deformable Convolution. jwyang/faster-rcnn.pytorch — This project is a PyTorch object detection framework that implements the Faster R-CNN architecture. It serves as a… szagoruyko/attention-transfer — Improving Convolutional Networks via Attention Transfer (ICLR 2017).
