• 4 min read
Launch: RF-DETR Keypoint in Roboflow
RF-DETR Keypoint beats YOLO26-pose on accuracy and speed, learns keypoint uncertainty, and is Apache 2.0. Label, train, and deploy in Roboflow.
• 9 min read
Off-the-Shelf vs. Custom Models for Industrial Computer Vision
SUMMARY
Use off-the-shelf models for rapid prototyping or when your task relies on detecting common, everyday objects that pre-trained models already recognize. However, to achieve the reliability required for specialized industrial production, you must deploy a custom, fine-tuned model trained specifically on your own domain'
• 4 min read
What Is YOLO27?
YOLO27 brings 3D perception to YOLO with YOLO-Depth and YOLO-StereoDepth.
• 12 min read
Train a Computer Vision Model with Roboflow Train
Discover how Roboflow Train automates computer vision model training. Eliminate infrastructure setup, leverage NAS, and deploy to production.
• 5 min read
What Is YOLO-StereoDepth?
YOLO-StereoDepth brings stereo depth for robotics to the YOLO family in September 2026. See what we know and how to read metric depth today.
• 4 min read
What Is YOLO-Anomaly?
YOLO-Anomaly brings purpose-built anomaly detection for manufacturing QA. See what to expect.
• 4 min read
What Is YOLO-VLM?
YOLO-VLM pairs a lightweight YOLO front-end with a deeper LLM layer for efficient vision-language pipelines.
• 4 min read
Roboflow MCP: Build Vision Apps with Claude, Codex, and More
The new Roboflow MCP Server connects Claude, Codex, and other AI agents to Roboflow so they can label visual data, train custom models, and build deployable vision pipelines.
• 8 min read
Download Pretrained YOLO Weights
Pretrained YOLO weights give your model a head start by initializing from COCO, a benchmark dataset of 80 real-world object categories. This guide covers what pretrained weights are, how Roboflow uses them for custom training, and how to get started.
• 11 min read
What is Mean Average Precision (mAP) in Object Detection?
What is mean average precision? How do we calculate mAP?
• 13 min read
How to Train a YOLO26 Oriented Bounding Box (OBB) Model
Learn how to train a YOLO26 Oriented Bounding Boxes model with a dataset labeled on Roboflow.
• 5 min read
Neural Architecture Search: Train the best vision model for your data
Balancing inference speed and accuracy just got easier. Learn how Neural Architecture Search evaluates thousands of configurations simultaneously to deliver the highest-performing model for your target hardware.
• 6 min read
Using Videos as Training Data
Turn video files into training data. Learn how to extract frames, set sampling rates, and use Roboflow to speed up computer vision labeling.
• 4 min read
How to Create a TFRecord File for Computer Vision and Object Detection
How to create a TFRecord file for object detection: what it is, when you need it, and how to export one from a labeled dataset in Roboflow.
• 25 min read
Gemini 3 Guide: Master Google’s Deep Think Model in Roboflow
Explore Gemini 3’s native multimodality and Deep Think reasoning. Learn how to deploy Gemini 3.1 Pro for object detection, OCR, and VQA using Roboflow Workflows and Playground.
• 4 min read
The Importance of Blur as an Image Augmentation Technique
Learn about the efficacy of blur as an image augmentation step in computer vision model training.
• 8 min read
How to Train an EfficientDet Object Detection Model with a Custom Dataset
Learn how to train an EfficientDet object detection model with a custom dataset.
• 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.
• 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.
• 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.
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