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Deformable-ConvNets is a computer vision framework and a collection of neural network components designed to implement deformable convolutional neural networks. It provides adaptive convolutional layers and pooling implementations that modify their receptive fields based on input features to better capture the geometry of objects within images. The project enables the use of learnable sampling offsets and modulation masks to align convolutional grids with target object shapes. It includes specialized tools for visualizing learned offsets in convolutions and pooling layers, allowing for the an
Super-Gradients is a PyTorch computer vision framework and training library designed for the full lifecycle of vision models. It functions as a deep learning model optimizer and a deployment toolkit for training and fine-tuning models across image classification, object detection, semantic segmentation, and pose estimation tasks. The project provides specific tools for model optimization, including teacher-student knowledge distillation and numerical precision compression to reduce memory and computational requirements. It also includes the implementation of the Yolo-NAS architecture for high
Image composition toolbox: everything you want to know about image composition/compositing or object/subject insertion/addition/compositing.
Source code for "Modeling Surface Appearance from a Single Photograph using Self-augmented Convolutional Neural Networks"
The main features of msraig/self-augmented-net are: Computer Vision Frameworks.
Projects with overlapping indexed features include: msracver/deformable-convnets — Deformable-ConvNets is a computer vision framework and a collection of neural network components designed to implement… deci-ai/super-gradients — Super-Gradients is a PyTorch computer vision framework and training library designed for the full lifecycle of vision… ethz-asl/rovio. ewenwan/apollo — An open autonomous driving platform 百度自动驾驶开源框架. ewenwan/houghforest. bcmi/libcom — Image composition toolbox: everything you want to know about image composition/compositing or object/subject…
