Open-source alternatives to DeconvNet

by Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia, details are in project page.

Seed, Expand, Constrain: Three Principles for Weakly-Supervised Image Segmentation

This repository serves as a centralized collection of state-of-the-art deep learning architectures and reference implementations designed for research and application development. It provides a comprehensive toolkit for computer vision and natural language processing, offering pre-built models and training pipelines for tasks ranging from image classification and object detection to complex sequence modeling. The project distinguishes itself by providing a flexible execution harness that manages the entire training lifecycle, including data ingestion and backpropagation. It supports scalable

This repository contains some models for semantic segmentation and the pipeline of training and testing models, implemented in PyTorch

This is the reference implementation of the models and code for the fully convolutional networks (FCNs) in the PAMI FCN and CVPR FCN papers:

Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang,and Hanqing Lu

This tutorial shows how to use Keras library to build deep neural network for ultrasound image nerve segmentation. More info on this Kaggle competition can be found on https://www.kaggle.com/c/ultrasound-nerve-segmentation.

This project is a modular PyTorch framework for training and evaluating object detection and instance segmentation models. It serves as a computer vision research tool and a deep learning inference engine designed to identify object locations, classes, and pixel-level masks within images. The framework implements a two-stage inference pipeline that utilizes region proposal networks and a symmetric mask-head architecture. It provides specialized capabilities for instance segmentation, object bounding box detection, and human pose estimation via anatomical keypoint detection. The system includ

This repository provides structured code examples and project templates designed for classroom instruction in machine learning and neural networks. It offers reference implementations of deep learning models for both computer vision and natural language processing tasks, built using PyTorch as the core framework. The codebase is organized as a modular project template with separate directories for data handling, model definitions, and training scripts, promoting reusability and clarity. It includes predefined pipelines for image classification and text processing, along with a command-line in

This is a PyTorch object detection framework that implements the Single Shot MultiBox Detector for identifying and localizing multiple objects within images and video. The project provides a neural network architecture designed for single-shot object detection, which predicts bounding boxes and class labels in one pass. The implementation includes a real-time object detector capable of processing live video streams to track and label objects across sequential frames. It also features a complete computer vision training pipeline for preparing image datasets and training model weights. The fra

Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)

An example of CoreML using a pre-trained VGG16 model

A PyTorch implementation of "DGC-Net: Dense Geometric Correspondence Network"

Convolutional Recurrent Neural Network

TPAMI 2023 ESI Highly Cited Paper SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation https://arxiv.org/abs/2204.08808

Pixel-wise segmentation on the VOC2012dataset dataset using pytorchpytorch.

Keras implementation of Deeplab v3+ with pretrained weights

Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions WACV 2023

PyTorch implementation of Deformable Convolution