This project inherits the property of our pytorch implementation of faster r-cnn. Hence, it also has the following unique features: It is pure Pytorch code. We convert all the numpy implementations to ...

This is a compelling opportunity to join a market leader where you will work at the intersection of data, machine learning, and business impact, using advanced analytics to drive strategic ...

Q.ANT successfully demonstrated a diffusion model and a recurrent neural network on its second-generation Native Processing Unit (NPU) at ISC High Performance 2026 in Hamburg. This proves that Q.ANT’s ...

Abstract: Faster region-based conventional neural network (Faster R-CNN) is a common algorithm for object detection that identifies the object and their location information through three steps: ...

Timely and accurate detection of foreign objects is crucial for the safe operation of transmission lines in power grid. Currently, object detection models have more and more parameters and their ...

Learn how Intersection over Union (IoU) works and how to implement it step-by-step using PyTorch. This guide covers everything from the basic concept to practical coding examples for object detection ...

A PyTorch implementation of EfficientDet from the 2019 paper by Mingxing Tan Ruoming Pang Quoc V. Le Google Research, Brain Team. The official and original: comming soon.

Abstract: This work presents to detect road signs in a few seconds for avoid accidents. For this work, there is utilized YOLO v5 object detection algorithm with PyTorch. In this method, image ...

In response to the challenges of small object detection in UAV aerial photography, such as complex backgrounds, tiny targets, dense targets, and edge deployment, the YOLOv11n model was improved.

This paper presents a mini-review of recent works in Salient Object Detection (SOD). First, We introduce SOD and its application in image processing tasks and applications. Following this, we discuss ...