Datature Blog

Visual Question Answering (VQA) enables AI models to answer natural language questions about images, powering use cases from healthcare and retail to accessibility and industrial inspection. In this article we show you how you can fine-tune your own VQA model with your dataset on Datature Vi.

Vision-language models can see, but without reasoning they often hallucinate, miss spatial details, or fail silently. This post shows how Chain-of-Thought prompting transforms VLMs into interpretable, step-by-step reasoners, with real examples of grounding, VQA, and physical AI using Cosmos Reason1 - and how to train and deploy them in practice.

Deploying Vision-Language Models is often harder than training them, but NVIDIA’s NIM simplifies everything by packaging the entire inference stack into a single optimized container. With Datature Vi’s integration, you can deploy your trained VLMs - like Cosmos Reason1 7B - in minutes using a consistent API and production-ready infrastructure, making large-scale inference fast, reliable, and easy to manage.

Comprehensive comparison of the latest advanced object tracking methods including ByteTrack, SAMBA-MOTR, CAMELTrack, Cutie, and DAM4SAM. Analysis covers tracking-by-detection vs detection-by-tracking paradigms, performance metrics, computational efficiency, and real-world applications in autonomous driving, surveillance, and video analytics.

SAM 3 is Meta’s next-generation segmentation model that shifts from geometric, prompt-based segmentation to concept-level understanding through Promptable Concept Segmentation, enabling open-vocabulary instance detection via text or visual exemplars.

Datature is excited to launch t-SNE Embedding Visualization, an intuitive way to explore image embeddings and reveal patterns in your datasets. By projecting high-dimensional vectors into an interactive 2D map, you can quickly spot duplicates, detect anomalies, understand distribution, and speed up labeling. With lasso selection, hover previews, and tunable parameters, the Projector turns raw embeddings into clear, interpretable clusters—helping teams across industries manage large-scale image data with ease.

Learn how to train Qwen2.5-VL to automatically detect and describe objects in images. This guide covers dataset preparation, training on consumer GPUs, and real-world results with detailed examples and troubleshooting tips

Learn how to enhance computer vision model performance by integrating metadata through early, middle, and late fusion techniques. Discover practical YOLO11 implementation examples and achieve up to 20% accuracy improvements in challenging classification tasks.

Class imbalance represents a significant challenge in machine learning where the distribution of classes in a dataset is heavily skewed. When one class substantially outnumbers others, models tend to develop a bias toward the majority class, potentially compromising their performance on minority classes. In this article we explore upsampling as an effective strategy to address class imbalance problems.