Object Detection
PaddlePaddle: Welcome to our guide of machine learning frameworks, where we’ll examine PaddlePaddle, TensorFlow, and PyTorch. Recent benchmark tests have revealed PaddlePaddle as a potential frontrunner, showcasing benchmark speeds that
This blogpost post explores different loss functions in object detection which include GIoU, IoU, and CIoU loss functions.
This research article dives into the intricacies of slicing aided hyper inference technique for small object detection.
In this article, we train the YOLO NAS model on a custom dataset, evaluate it and run inference using the trained model.
In this article, we explore how to train the YOLOv8 instance segmentation models on custom data.
YOLO-NAS is the new real-time SOTA object detection model. YOLO-NAS models outperform YOLOv7, YOLOv8 & YOLOv6 3.0 models in terms of mAP and inference latency.
In this post, we will learn how to perform object detection with TensorFlow Hub pre-trained models. TensorFlow Hub is a library and platform designed for sharing, discovering, and reusing pre-trained
This article shows the steps for deploying a deep learning model on HuggingFace Spaces using Gradio.
In this article, we train YOLOv8 on a custom pothole detection dataset using the Ultralytics YOLO package.
In this article, we explore the Ultralytics YOLOv8 models for object detection, instance segmentation, and image classification.
In this article, we explore the YOLOv5 instance segmentation architecture and run inference on several videos and images.
YOLOR, inspired by how humans combine knowledge, is an object detection model that pushes the boundaries of real-time detection with improved speed & accuracy.
