Code release for "Omni3D A Large Benchmark and Model for 3D Object Detection in the Wild"

YOLO 3D Object Detection for Autonomous Driving Vehicle

PyTorch implementation for 3D Bounding Box Estimation Using Deep Learning and Geometry

Unofficial Pytorch Implementation for MonoCon(AAAI, 2022)

[RA-L 2022] SGM3D: Stereo Guided Monocular 3D Object Detection

🕶 A curated list of Tiny Object Detection papers and related resources.

Awesome Monocular 3D detection

[CVPR2019]Occlusion-Net: 2D/3D Occluded Keypoint Localization Using Graph Networks

This project implements visual odometry to estimate the trajectory of a self-driving car.

official implementation of the spatial-temporal attention neural network (STANet) for remote sensing image change detection

moai is a PyTorch-based AI Model Development Kit (MDK) created to improve data-driven model workflows, design and reproducibility.

A paper list of some recent Transformer-based CV works.

Single-Stage Keypoint-based Category-level Object Pose Estimation from an RGB Image (ICRA 2022)

(TGRS 2022) Paper: Interior Attention-Aware Network for Infrared Small Target Detection

Techniques for deep learning with satellite & aerial imagery

Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning

[CVPR 2022 Oral] QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection

Large Scale Non maximum Suppression. This project implements a O(nlog(n)) approach for non max suppression, useful for object detection ran on very large images such as satellite or histology, when…

Models and examples built with TensorFlow

Object Detection Metrics. 14 object detection metrics: mean Average Precision (mAP), Average Recall (AR), Spatio-Temporal Tube Average Precision (STT-AP). This project supports different bounding b…

Most popular metrics used to evaluate object detection algorithms.

Using yolov3 & yolov4 weights objects are being detected from live video frame along with the measurement of the object from the camera without the support of any extra hardware device.

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

A tensorflow implementation for Unsupervised vessel segmentation based on ADDA

SOTA medical image segmentation methods based on various challenges

Supervised and unsupervised loss functions for image segmentation based on the classical FCM objective function. (TensorFlow and PyTorch)

An unsupervised (or self-supervised) loss function for binary image segmentation (TensorFlow)