msracver/Deformable-ConvNets
Deformable ConvNets
Deformable-ConvNets es un framework de visión artificial y una colección de componentes de redes neuronales diseñados para implementar redes neuronales convolucionales deformables. Proporciona capas convolucionales adaptativas e implementaciones de pooling que modifican sus campos receptivos basándose en las características de entrada para capturar mejor la geometría de los objetos dentro de las imágenes.
El proyecto permite el uso de offsets de muestreo aprendibles y máscaras de modulación para alinear las rejillas convolucionales con las formas de los objetos objetivo. Incluye herramientas especializadas para visualizar los offsets aprendidos en las capas de convolución y pooling, permitiendo analizar cómo la red adapta su campo receptivo espacial.
Estas capacidades se aplican para mejorar la precisión en la detección de objetos y refinar la segmentación semántica. El framework admite la extracción de características de regiones de interés mediante pooling deformable para alinear las áreas de muestreo con los límites reales de los objetos.
La implementación incluye un pipeline de entrenamiento para ejecutar y evaluar estas arquitecturas de red especializadas.
Features
- Learnable Spatial Offsets - Enables the use of learnable 2D offsets to adjust convolutional grids to match object shapes in images.
- Deformable ROI Pooling - Implements a deformable ROI pooling mechanism that aligns feature extraction grids with the actual geometry of target objects.
- Deformable - Implements deep learning architectures that adapt convolutional sampling grids to fit the geometry of objects.
- Dynamic Receptive Field Modeling - Uses learnable offsets in convolutional layers to dynamically change how features are sampled based on image content.
- Adaptive Convolutional Layers - Provides neural network layers that modify their receptive fields to better capture complex object shapes.
- Deformable Convolutions - Provides deformable convolutional layers that adjust sampling grids to improve object detection and semantic segmentation.
- Modulated - Combines learnable offsets and modulation mechanisms to fit the receptive field accurately to object shapes.
- Modulated Receptive Fields - Implements learnable modulation masks to scale pixel contributions and ignore irrelevant background noise in receptive fields.
- Computer Vision Frameworks - Provides a framework of tools for improving object detection and segmentation via learnable sampling offsets.
- Adaptive Sampling Grids - Modifies spatial sampling patterns based on input geometry to provide a flexible and adaptive receptive field.
- CNN Kernel Visualizations - Provides a utility for rendering learned convolutional offsets to analyze adaptive spatial sampling.
- 2D Convolutional Rendering - Renders learned offsets of convolutions and pooling layers to visualize how the network adapts its spatial sampling.
- Offset Visualizations - Provides specialized tools to render and analyze how the network adapts its spatial receptive field through learned offsets.
- Object Detection - Improves the precision of object detection by allowing convolutional grids to adapt to object shapes.
- Semantic Image Segmentations - Refines semantic image segmentation by adjusting sampling grids to better fit object boundaries.
- CUDA Compute Kernels - Provides high-performance C++ CUDA compute kernels to execute the sampling and interpolation logic on GPUs.
- Object Detection - Deformable convolutional networks for detection.
- Segmentation Architectures - Deformable convolutional networks for segmentation tasks.
