• 8 min read
How to Train a VGG-16 Image Classification Model on Your Own Dataset
The VGG16 model explained: 16 layers, 3x3 filters, 92.7% ImageNet top-5, plus how to train an image classifier on your own data, no GPU.
• 15 min read
Roboflow Training Graphs Guide
Learn how to read Roboflow training graphs. Understand what each curve means, how the underlying metrics are computed, and how to diagnose common training problems.
• 5 min read
Why and How to Implement Random Crop Data Augmentation
Learn how to apply a random crop data augmentation to images for use in training computer vision models.
• 7 min read
EfficientDet vs YOLO for Object Detection
EfficientDet vs YOLO compared on architecture, speed, accuracy, and licensing, plus why RF-DETR is the stronger detector to deploy today.
• 10 min read
Training a TensorFlow MobileNet Object Detection Model with a Custom Dataset
Train a TensorFlow object detection model on a custom dataset the modern way: label, train RF-DETR, and deploy in Roboflow, no TFRecords.
• 7 min read
Best Multimodal Models in 2026
From SAM 3’s record-breaking segmentation speed to Gemini 3’s massive 2-million-token context window, explore the top models that can "see," reason, and deploy in production today.
• 7 min read
Segment Anything
This guide breaks down the SAM lineage - from SAM 1’s 50ms CPU inference to SAM 3’s open-vocabulary "GPT moment" - showing you how to deploy these foundation models into your production workflows today.
• 9 min read
How to Train an EfficientNet Model with a Custom Dataset
Learn how to train an EfficientNet model with a custom dataset.
• 13 min read
What Is YOLOv5? Architecture, Sizes, and How It Works
In this article, we discuss what is new in YOLOv5, how the model compares to YOLOv4, and the architecture of the new v5 model.
• 10 min read
Open-Vocabulary Object Detection Using Qwen3-VL in Google Colab
See how to run Qwen3-VL in Google Colab to build a prompt-driven detection pipeline that turns “find a yellow taxi” into structured, production-ready results.
• 8 min read
Using Vision-Language Models for Image Understanding
Vision-language models make image understanding fast and accessible. By using simple text prompts, teams can explore datasets, discover labels, spot errors, and speed up annotation early, before training, building stronger foundations for reliable computer vision systems.
• 6 min read
How to Use Qwen3-VL in Roboflow
In this guide, build an image-understanding workflow in Roboflow that turns Qwen3-VL into a production-ready vision agent you can run via API or on your own GPU.
• 5 min read
Train a YOLO26 Instance Segmentation Model with Custom Data
SUMMARY
This tutorial walks through fine-tuning YOLO26 for instance segmentation on the Car Part Detection dataset from Roboflow Universe, using a Google Colab notebook with the Ultralytics library. Unlike object detection bounding boxes, instance segmentation identifies the exact pixels belonging to each object, which matters for applications like medical
• 4 min read
How to Train a YOLO26 Object Detection Model with Custom Data
SUMMARY
This tutorial covers fine-tuning a YOLO26 object detection model on the Basketball-Player-Detection dataset from Roboflow Universe, which contains 654 annotated images of NBA players, referees, and basketballs. Using a Google Colab notebook with the Ultralytics library, the guide walks through GPU environment setup, dataset download via
• 8 min read
Depth Anything 3 for Depth Estimation
In this guide, learn what makes DA3 different, where it shines, and how to build a working depth estimation pipeline in Roboflow Workflows that generates real 3D insight in minutes.
• 10 min read
What is YOLO? The Ultimate Guide
Learn about the history of the YOLO family of objec tdetection models, extensively used across a wide range of object detection tasks.
• 13 min read
Object Detection vs Vision-Language Models: When Should You Use Each?
Object detection and vision-language models solve different problems. This guide explains when to use each. And how speed, cost, and reliability differ.
• 6 min read
How to Train a YOLOv5 Model On a Custom Dataset
Learn how to train a YOLOv5 model on a custom dataset.
• 7 min read
Try Gemini 3 Pro From Google
Learn how Gemini 3 Pro sets new vision benchmarks. Then try and compare it against other top vision models using Roboflow Playground.
• 5 min read
How to Fine-Tune Claude 3.7 Sonnet With Roboflow
Claude 3.7 Sonnet is one of the most powerful multimodal models for visual reasoning, and now you can customize it for your use case with Roboflow Workflows. Learn how to fine-tune Sonnet for structured JSON outputs and deploy a production-ready visual Q&A tool.
• 17 min read
Best Defect Detection Algorithms for Manufacturing
This guide breaks down the best defect detection algorithms for manufacturing and shows why instance segmentation is now the standard for high-precision quality control.
• 4 min read
Introducing Roboflow Rapid: Text prompt to vision model in minutes
SUMMARY
Roboflow Rapid is a prompt-based model creation engine that takes you from a text description and a handful of images or a short video to a deployed custom model without manual labeling. You type a prompt describing what to detect, Rapid automatically finds and annotates matching objects, and
• 15 min read
Meta SAM 3D: Introduction
Understand what is SAM 3d and learn how to use SAM 3D + Roboflow to generate synthetic data and train a computer vision model.
• 9 min read
How to Fine-Tune Segment Anything 3 (SAM 3) on a Custom Dataset
Learn how to fine-tune SAM-3 for image segmentation with a custom dataset.
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