Category: object-detection
-
Detecting and Editing Visual Objects with Gemini
Detecting and Editing Visual Objects with Gemini A practical guide to identifying, restoring, and transforming elements within your images The post Detecting and Editing Visual Objects with Gemini appeared first on Towards Data Science. Laurent Picard Go to original source
-
YOLOv2 & YOLO9000 Paper Walkthrough: Better, Faster, Stronger
YOLOv2 & YOLO9000 Paper Walkthrough: Better, Faster, Stronger From YOLOv1 to YOLOv2: prior box, k-means, Darknet-19, passthrough layer, and more The post YOLOv2 & YOLO9000 Paper Walkthrough: Better, Faster, Stronger appeared first on Towards Data Science. Muhammad Ardi Go to original source
-
SAM 3 vs. Specialist Models — A Performance Benchmark
SAM 3 vs. Specialist Models — A Performance Benchmark Why specialized models still hold the 30x speed advantage in production environments The post SAM 3 vs. Specialist Models — A Performance Benchmark appeared first on Towards Data Science. Pushpak Bhoge Go to original source
-
YOLOv1 Paper Walkthrough: The Day YOLO First Saw the World
YOLOv1 Paper Walkthrough: The Day YOLO First Saw the World A detailed walkthrough of the YOLOv1 architecture and its PyTorch implementation from scratch The post YOLOv1 Paper Walkthrough: The Day YOLO First Saw the World appeared first on Towards Data Science. Muhammad Ardi Go to original source
-
RF-DETR Under the Hood: The Insights of a Real-Time Transformer Detection
RF-DETR Under the Hood: The Insights of a Real-Time Transformer Detection From rigid grids to adaptive attention, this is the evolutionary path that made detection transformers fast, flexible, and formidable. The post RF-DETR Under the Hood: The Insights of a Real-Time Transformer Detection appeared first on Towards Data Science. David Redó Nieto Go to original…
-
How to Import Pre-Annotated Data into Label Studio and Run the Full Stack with Docker
How to Import Pre-Annotated Data into Label Studio and Run the Full Stack with Docker From VOC to JSON: Importing pre-annotations made simple The post How to Import Pre-Annotated Data into Label Studio and Run the Full Stack with Docker appeared first on Towards Data Science. Yagmur Gulec Go to original source
-
From Rules to Relationships: How Machines Are Learning to Understand Each Other
From Rules to Relationships: How Machines Are Learning to Understand Each Other Using knowledge graphs to handle the unexpected in semantic communication The post From Rules to Relationships: How Machines Are Learning to Understand Each Other appeared first on Towards Data Science. Shireesh Kumar Singh Go to original source
-
From Fuzzy to Precise: How a Morphological Feature Extractor Enhances AI’s Recognition Capabilities
From Fuzzy to Precise: How a Morphological Feature Extractor Enhances AI’s Recognition Capabilities Introduction: Can AI really distinguish dog breeds like human experts? One day while taking a walk, I saw a fluffy white puppy and wondered, Is that a Bichon Frise or a Maltese? No matter how closely I looked, they seemed almost identical.…
-
From Fuzzy to Precise: How a Morphological Feature Extractor Enhances AI’s Recognition Capabilities
From Fuzzy to Precise: How a Morphological Feature Extractor Enhances AI’s Recognition Capabilities Introduction: Can AI really distinguish dog breeds like human experts? One day while taking a walk, I saw a fluffy white puppy and wondered, Is that a Bichon Frise or a Maltese? No matter how closely I looked, they seemed almost identical.…
-
Custom Training Pipeline for Object Detection Models
Custom Training Pipeline for Object Detection Models What if you want to write the whole object detection training pipeline from scratch, so you can understand each step and be able to customize it? That’s what I set out to do. I examined several well-known object detection pipelines and designed one that best suits my needs…
-
Zero-Shot Player Tracking in Tennis with Kalman Filtering
Zero-Shot Player Tracking in Tennis with Kalman Filtering Automated tennis tracking without labels: GroundingDINO, Kalman filtering, and court homography https://medium.com/media/6f735abc63f905de122bb8a0679f97fd/href With the recent surge in sports tracking projects, many inspired by Skalski’s popular soccer tracking project, there’s been a notable shift towards using automated player tracking for sport hobbyists. Most of these approaches follow a…
-
Mastering Sensor Fusion: Color Image Obstacle Detection with KITTI Data — Part 2
Mastering Sensor Fusion: Color Image Obstacle Detection with KITTI Data — Part 2 Mastering Sensor Fusion: Color Image Obstacle Detection with KITTI Data — Part 2 How to use color image data for object detection in the context of obstacle detection The concept of sensor fusion is a decision-making mechanism that can be applied to different problems and using different…
-
CV VideoPlayer — Once and For All
CV VideoPlayer — Once and For All CV VideoPlayer — Once and For All A Python video player package made for computer vision research Image by author When developing computer vision algorithms, the journey from concept to working implementation often involves countless iterations of watching, analyzing, and debugging video frames. As I dove deeper into computer vision projects, I found myself repeatedly…
-
How to Solve a Simple Problem With Machine Learning
How to Solve a Simple Problem With Machine Learning A technical walkthrough of lesson one Continue reading on Towards Data Science » Oscar Leo Go to original source