• 8 min read
Outsourced Data Labeling in Roboflow
Roboflow offers outsourced labeling via approved partners. Receive the custom annotations you need to train high-quality models.
• 10 min read
The Difference Between Missing and Null Annotations
Learn the difference between missing versus null annotations, including how they are represented in VOC XML and COCO JSON formats, and how their misidentification can be addressed using Roboflow tools to improve dataset quality.
• 9 min read
How to Create a Synthetic Dataset for Computer Vision
Accelerate computer vision model development by generating synthetic images. Learn to build an automated Stability AI pipeline in Roboflow.
• 7 min read
How to Use Tiling During Inference
Discover how image tiling improves small object detection. Learn to apply it during training and inference using Roboflow Workflows.
• 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.
• 4 min read
How Flip Augmentation Improves Model Performance
How flip augmentation improves model performance, when horizontal or vertical flips help, when to avoid them, and how to apply flips in Roboflow.
• 4 min read
LabelImg for Labeling Object Detection Data
How to use LabelImg to label object detection data, labeling best practices, and a faster path to a trained model with Roboflow.
• 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.
• 5 min read
Why to Add Noise to Images for Machine Learning
Learn why adding noise can be effective as an image augmentation in computer vision modeling.
• 7 min read
VoTT for Image Annotation and Labeling
A guide on using VoTT to label your own computer vision dataset.
• 3 min read
When Should I Auto-Orient My Images?
Learn when you should auto-orient images for use in training computer vision models.
• 5 min read
Why and How to Implement Random Rotate Data Augmentation
Learn how to apply a random rotate data augmentation to images for use in training computer vision models.
• 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.
• 6 min read
When to Use Contrast as a Preprocessing Step
See how contrast preprocessing improves edge detection and boosts model accuracy.
• 7 min read
How to Label Image Data for Computer Vision Models
This guide discusses what image labeling is and how to effectively label images for use in training computer vision models.
• 6 min read
You Might Be Resizing Your Images Incorrectly
Resizing images incorrectly can distort objects and degrade model performance. Learn how to resize images without distortion: aspect ratios, padding strategies, and practical tips to ensure your models learn from clean, consistent visuals.
• 11 min read
How to Grow Small Vision Datasets with SAM 3 and Synthetic Augmentation
Traditional image augmentation can make datasets bigger without making models better. In this tutorial, we show how to use SAM 3 to generate clean instance segmentation masks, apply segmentation-aware augmentation in Roboflow, and measure whether those changes actually improve model performance.
• 10 min read
How to Convert Annotations from PASCAL VOC XML to COCO JSON
Convert from VOC XML to COCO JSON (or any format) in four clicks.
• 3 min read
When to Use Grayscale as a Preprocessing Step
Grayscale allows our models to be more computationally efficient. So when **shouldn't** we grayscale our images?
• 9 min read
What Is Image Preprocessing and Augmentation?
Understanding image preprocessing and augmentation options is essential to making the most of your training data.
• 12 min read
Data Annotation for High-Performing Computer Vision Models
Learn all about data annotation, from what it is and how it works, to common challenges, best practices, and the tools that can streamline the process.
• 7 min read
5 Best Image Annotation Tools in 2026
Explore the top five image annotation tools you can use to label data for your next computer vision project.
• 8 min read
5 Strategies for Handling Unbalanced Classes in Machine Learning
SUMMARY
Class imbalance causes models to underperform on minority classes because the training distribution does not reflect the real-world distribution of what matters. Five strategies address this: gathering more data from underrepresented classes, generating synthetic samples through augmentation, undersampling the majority class via random selection, oversampling the minority class
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