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aerial-semantic-segmentation-for-drone-imagery-unet-cnn

Deep learning model for Aerial Semantic Segmentation of Drone Imagery using a U-Net CNN architecture.

attention-module

Official PyTorch code for "BAM: Bottleneck Attention Module (BMVC2018)" and "CBAM: Convolutional Block Attention Module (ECCV2018)"

autonomousdriving

cabinet

CABiNet: Efficient Context Aggregation Network for Low-Latency Semantic Segmentation (ICRA2021)

crack-segmentation

🚧 | Road crack segmentation using PyTorch

deep-residual-learning

PyTorch implementations of the deep residual networks published in "Deep Residual Learning for Image Recognition" by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun

digitalgarden

Personal knowledge garden dedicated to AI, ML, AI for Earth Sciences, AI for good, Machine Learning and Data Science

drone-images-semantic-segmentation

Multi-class semantic segmentation performed on "Semantic Drone Dataset."

droneimage-segmentation

This is a PyTorch-based project for drone image segmentation. The goal of this project is to segment objects and regions of interest within aerial images captured by drones. Image segmentation is a crucial task in computer vision and has various applications, including agriculture, urban planning, and environmental monitoring.

dronet

DroNet: Efficient convolutional neural network detector for Real-Time UAV applications

ee8204-resunet

First implementation of EE8204 Course Project. This code aims at utilizing a Deep Residual U-Net to segment roads from satellite imagery

efficientdet.pytorch

Implementation EfficientDet: Scalable and Efficient Object Detection in PyTorch

enet-real-time-semantic-segmentation

ENet - A Neural Net Architecture for real time Semantic Segmentation

epnetv2

EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection (TPAMI-2022)

geoseg

UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery, ISPRS. Also, including other vision transformers and CNNs for satellite, aerial image and UAV image segmentation.

json2yolo

Convert JSON annotations into YOLO format.

kitti-roadseg

A course project for road segmentation using a U-Net Convolutional Neural Network on the KITTI ROAD 2013 dataset

lidar2map

The official implementation of "LiDAR2Map: In Defense of LiDAR-Based Semantic Map Construction Using Online Camera Distillation" (CVPR 2023)

midas

Code for robust monocular depth estimation described in "Ranftl et. al., Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer, TPAMI 2022"

mono-semantic-maps

mono3dplidar

Official PyTorch Implementation for "Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud", ICCVW 2019

plard

Progressive LiDAR Adaptation for Road Detection

pointpainting

This repository is an open-source PointPainting package which is easy to understand, deploy and run!

pointrcnn

PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud, CVPR 2019.

pretrained-yolov8-network-for-object-detection

YOLO v8 inference in MATLAB for Object Detection with yolov8n, yolov8s, yolov8m, yolov8l, yolov8x, networks

pytorch-retinanet

Pytorch implementation of RetinaNet object detection.

pytorch-unet

Simple PyTorch U-Net for semantic segmentation of fish images.

pytorch-unet-new

PyTorch implementation of the U-Net for image semantic segmentation with high quality images

real-time-road-segmentation-from-uav-data

recursive-noise-diffusion