Projects sharing features with Examples

Dilated Residual Networks

A memory-efficient implementation of DenseNets

Non official pytorch implementation of i-Resnet, invertible residual networks.

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

This is a PyTorch-based training pipeline designed for reproducible image classification benchmarking on the CIFAR-10 dataset. It integrates GPU-accelerated computation, data augmentation, learning rate scheduling, and checkpointing to produce consistent accuracy measurements across multiple ResNet architectures. The project distinguishes itself by providing a fixed-architecture benchmark suite that trains a predefined set of ResNet variants, from ResNet18 through ResNet152, on CIFAR-10. It implements a step-based learning rate decay schedule at predetermined epochs to stabilize convergence,

Faster RCNN with PyTorch

Unsupervised Video Interpolation using Cycle Consistency

PyTorch implementation of Deformable Convolution

This repo is used to research convolutional networks primarily for computer vision tasks. For this purpose, the repo contains (re)implementations of various classification, segmentation, detection, and pose estimation models and scripts for training/evaluating/converting.

MeshCNN is a general-purpose deep neural network for 3D triangular meshes, which can be used for tasks such as 3D shape classification or segmentation. This framework includes convolution, pooling and unpooling layers which are applied directly on the mesh edges.

This project is a library of pretrained computer vision architectures and backbones for image classification and feature extraction. It serves as a comprehensive model zoo and collection of standardized image encoders, including ResNet, Vision Transformers, and EfficientNet, for use in visual analysis and as backbones for object detection and image segmentation. The library provides a framework for distributed training and evaluation of image models using advanced data augmentation and optimization scripts. It includes a dedicated toolset for converting trained PyTorch vision models into the

PyTorch code for the "Deep Neural Networks with Box Convolutions" paper

an implementation of Video Frame Interpolation via Adaptive Separable Convolution using PyTorch

Improving Convolutional Networks via Attention Transfer (ICLR 2017)

Training Very Deep Neural Networks Without Skip-Connections

PyTorch and Tensorflow functional model definitions

This is a PyTorch CNN visualization toolkit designed for neural network interpretability. It provides a set of tools to explain model decisions and analyze the internal behavior of convolutional neural networks through the visualization of activations, gradients, and filters. The project implements specialized techniques for synthesizing representative images, including Deep Dream optimizations to amplify patterns and class-specific image generation via input optimization. It also features a saliency map generator that produces gradient-based heatmaps to identify the specific image regions in

Wide Residual Networks (WideResNets) in PyTorch