• 7 min read
Launch: Train and Deploy RF-DETR Models with Roboflow
Learn how to train RF-DETR models with Roboflow Train and deploy RF-DETR models with Roboflow Workflows and Inference.
• 11 min read
How to Train RF-DETR on a Custom Dataset
Learn how to train an RF-DETR model on a custom dataset.
• 7 min read
Launch: Fine-Tune and Deploy Qwen2.5-VL Models with Roboflow
Learn how to fine-tune and deploy Qwen2.5-VL models with Roboflow.
• 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
• 8 min read
What is Few-Shot Learning?
In this blog post, we discuss what few-shot learning is, architectural approaches for implementing few-shot learning, and specific implementations of few-shot learning techniques.
• 5 min read
Launch: Fine-Tune PaliGemma-2 for VQA with Roboflow
Learn how to fine-tune PaliGemma-2 for VQA with Roboflow.
• 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.
• 5 min read
Launch: Fine-Tune Florence-2 for VQA with Roboflow
Learn how to fine-tune Florence-2 for visual question answering (VQA) using Roboflow.
• 8 min read
How to Fine-Tune SAM-2.1 on a Custom Dataset
Learn how to fine-tune the Segment Anything 2.1 (SAM-2.1) model on a custom dataset.
• 11 min read
How to Fine-Tune GPT-4o for Object Detection
Learn how to fine-tune GPT-4o to detect the location of objects in images.
• 6 min read
How to Train YOLOv11 Instance Segmentation on a Custom Dataset
Learn how to fine-tune a YOLOv11 instance segmetnation model with a custom dataset and deploy the model with Roboflow Inference.
• 10 min read
How to Train and Deploy YOLOv10 with Intel Emerald Rapids
Learn how to train and deploy YOLOv10 models on Intel Emerald Rapids systems.
• 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.
• 11 min read
What is Active Learning? The Ultimate Guide.
In this guide, we discuss what active learning is, types of active learning, and walk through an example of active learning in practice.
• 8 min read
Camera Calibration in Sports with Keypoints
SUMMARY
Accurate player tracking in sports footage requires mapping pixel coordinates from a moving camera to real-world field positions, and this tutorial covers how to do that by training a YOLOv8 keypoint detection model to recognize characteristic landmarks on a soccer pitch, then using those detections to compute a
• 8 min read
What is the Open Images Dataset? A Deep Dive.
SUMMARY
Open Images V7, released by Google in 2022, contains over nine million annotated images spanning nearly 20,000 categories and supports six annotation types: image-level labels, bounding boxes, segmentation masks, visual relationships, localized narratives, and point-level labels. That breadth makes it one of the few public datasets
• 11 min read
How to Train RT-DETR on a Custom Dataset with Transformers
SUMMARY
RT-DETR (Real-Time DEtection TRansformer), developed by Peking University and Baidu, is a transformer-based object detection model that targets competitive speed and accuracy compared to YOLO-family models. This tutorial covers fine-tuning RT-DETR on a custom dataset sourced from Roboflow Universe, using the HuggingFace Transformers
• 5 min read
How to Use GPT-4 To Extract Handwritten Text from Images
SUMMARY
This tutorial builds a pipeline that combines a custom object detection model trained in Roboflow with GPT-4 Vision inside a Roboflow Workflow to extract and structure handwritten information from homework booklets. The object detection model localizes handwritten marks, and GPT-4 Vision reads time and name fields, with
• 12 min read
How to Fine-tune Florence-2 for Object Detection Tasks
This tutorial will show you how to fine-tune Florence-2 on object detection datasets to improve model performance for your specific use case.
• 7 min read
What is YOLOv10? An Architecture Deep Dive
Learn about the main architectural components of YOLOv10 that contribute to the model's state-of-the-art speed and accuracy.
• 6 min read
How to Train a YOLOv10 Model on a Custom Dataset
Learn how to train a YOLOv10 model using a custom dataset.
• 7 min read
What is New in YOLOv9? An Architecture Deep Dive.
Learn what YOLOv9 is and what architectural features allow YOLOv9 to achieve strong performance on object detection and segmentation tasks.
• 8 min read
Finetuning Moondream2 for Computer Vision Tasks
In this guide, we finetune and improve Moondream2, a small, local, fast multimodal Vision Language Model, for a computer vision task.
• 5 min read
What is YOLOv3? An Introductory Guide.
Learn what YOLOv3 is and the notable architectural eatures of this model.
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