• 6 min read
How to Build and Run an Aimbot
Learn how to build and run an aimbot using computer vision.
• 12 min read
Vision Transformer vs. CNN for Object Detection
This guide explains how CNNs and ViTs differ, when to use each, and how to train and compare them directly inside Roboflow so you can choose the best architecture for your detection task.
• 6 min read
Launch: Use Segment Anything 3 (SAM 3) with Roboflow
SUMMARY
Roboflow has integrated SAM 3, Meta's foundation model for Promptable Concept Segmentation, across every major part of its platform, covering the Playground for interactive testing, the inference API, edge and private-cloud deployment, Roboflow Workflows for building production pipelines, and the Label Assist annotation tool for accelerating
• 11 min read
Python Object Detection
Learn Python object detection step-by-step using Roboflow Inference. Run RF-DETR and fine-tuned models from Roboflow Universe on images and videos with ease.
• 9 min read
How to Detect, Track, and Identify Basketball Players with Computer Vision
Build a computer vision pipeline that detects, tracks, and identifies NBA players during a game. Using models like RF-DETR, SAM2, SigLIP, SmolVLM2, and ResNet, the system handles motion blur, occlusion, and uniform similarity to reliably map jersey numbers to player identities.
• 7 min read
How to Build a Custom Object Detection Pipeline
Building a custom object detection pipeline lets you move beyond generic models and tailor vision AI to your specific needs. In this guide, we show how to create a complete pipeline with Roboflow, covering dataset creation, training, evaluation, and deployment.
• 15 min read
Object Counting with Computer Vision
Understand what is object counting in computer vision, challenges in object counting, real-world use-cases of object counting and learn how to perform object counting in image and video.
• 5 min read
GPT-5 for Vision: Results from 80+ Real-World Tests
SUMMARY
Roboflow tested GPT-5 across more than 80 structured visual tasks spanning document understanding, defect detection, object counting, and the RF100-VL object detection benchmark. On Vision Checkup, GPT-5 tied for first place with GPT o4 Mini, driven largely by strong spatial reasoning, but scored only 4 of
• 9 min read
How to Create a YOLOv11 Android App
SUMMARY
This tutorial walks through training a custom YOLOv11 object detection model with Roboflow and deploying it directly on an Android device to build a real-time coin counting app. The model weights are converted from a .pt file to TorchScript format so they can run on-device without a
• 9 min read
How to Build an iOS App with Visual AI Capabilities
SUMMARY
This tutorial walks through building an iOS app that detects glasses in real time using a custom RF-DETR Nano model trained in Roboflow and deployed via the roboflow-swift SDK. RF-DETR Nano is chosen specifically for its low latency on live camera streams, making it well suited
• 8 min read
Detect NBA 3 Second Violations with AI
SUMMARY
Automatically detecting NBA 3-second paint violations requires solving several distinct sub-problems at once: tracking each player across frames, identifying the painted zone, and counting how long each player stays inside it. This tutorial builds that system by combining player tracking with Meta's SAM2, court keypoint
• 5 min read
Use Qwen2.5-VL for Zero-Shot Object Detection
SUMMARY
Qwen2.5-VL is an open source vision-language model available in 3B, 7B, and 72B sizes that can locate and label objects in an image from a plain-text prompt, with no labeled training data or fine-tuning required. This tutorial runs the 7B variant on a T4
• 19 min read
YOLO Object Detection: An Introduction
Understand what is YOLO for object detection, how it works, what are different YOLO models and learn how to use YOLO with Roboflow.
• 8 min read
What Is Object Detection? How It Works and Why It Matters
In this guide, we discuss what object detection is, how it works, how to label and augment data for object detection models, and more.
• 11 min read
SORT Explained: Real-Time Object Tracking in Python
blog post explains how to use the SORT object tracking algorithm for real-time tracking in computer vision projects, highlighting its lightweight, efficient design and easy implementation with the Trackers library in Python.
• 20 min read
Object Detection Metrics
Understand what key object detection metrics are. Learn why they're used and how to use them.
• 9 min read
Using Computer Vision to Develop a Robotic Arm Poker Dealer
Learn how the vision system of an acting poker dealer arm was built by the University of Manchester Robotics Society.
• 5 min read
How to Train a YOLOv12 Object Detection Model on a Custom Dataset
Learn how to train a YOLOv12 object detection model on a custom dataset.
• 5 min read
Count Objects on a Conveyor Belt Using Computer Vision
SUMMARY
This tutorial builds an end-to-end system for counting bolts and nuts on a moving conveyor belt, covering every stage from data collection to local deployment. Video frames are annotated using Roboflow's auto-labeling with Grounding DINO, a custom detection model is trained in the cloud,
• 13 min read
How to Fine-tune PaliGemma 2
Learn how to fine-tune PaliGemma 2 to extract data from an image in JSON format.
• 3 min read
How to Remove Background of an Image with Computer Vision
Learn how to build your own background removal application using computer vision.
• 3 min read
Motion Detection with Computer Vision
SUMMARY
This tutorial shows how to filter object detection to only moving objects by comparing consecutive video frames with a pixel-differencing function. The approach uses inferenceJS to run a Roboflow Universe model, then applies a bounding box drawn from detected motion to suppress detections of stationary objects. Configurable thresholds
• 4 min read
How to Draw a Bounding Box with Python
In this post, we discuss how to use the Roboflow supervision library to draw and fill a bounding box in Python.
• 7 min read
Ball Tracking in Sports with Computer Vision
Ball tracking is crucial for AI systems to analyze sports effectively, but it's challenging due to factors like the ball's small size, high velocity, complex backgrounds, similar-looking objects, and varying lighting. This tutorial will teach you how to overcome these challenges.
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