Voxel51 blog

Physical AI is AI that perceives, understands, and acts in the physical world: robots, autonomous vehicles, drones, and the sensor data they depend on. These posts show how teams use FiftyOne to curate and evaluate multimodal data from cameras, LiDAR, and radar, turning raw sensor logs into better robot policies and safer autonomous systems. Expect deep dives on vision-language-action (VLA) models, world foundation models, simulation, and synthetic data, plus hands-on walkthroughs you can run yourself.

Visual AI is how machines learn to see, from image classification, object detection, segmentation, and anomaly detection to embeddings, zero-shot learning, and vision-language models. These posts show you how to build higher-performing visual AI systems with FiftyOne, whether you're debugging a detection model, comparing image embedding models, or putting the newest VLM through its paces on real data.

Search is how you find the data that matters in datasets too large to review by hand. FiftyOne makes visual and multimodal data searchable by natural language, visual similarity, metadata, and events, powered by embeddings and vector search. These posts show you how to surface edge cases, failure modes, and rare scenarios across images, video, and sensor data, and how to visualize what you find in context.

Better data beats more data. Data curation is the craft of finding the samples that matter: surfacing edge cases, removing duplicates and leaky splits, balancing classes, and mining rare scenarios with embeddings, clustering, and similarity search. These posts show you how to curate visual and multimodal datasets with FiftyOne so your models train on the data that actually improves them.

Great models start with great labels. These posts cover the full labeling workflow: auto-labeling with foundation models, VLM-assisted and human-in-the-loop pipelines, agentic labeling with the FiftyOne Agent, and finding label mistakes before they poison training. Learn how FiftyOne connects annotation to curation and evaluation so your labels improve in the same loop as your models.

A single accuracy number hides more than it reveals. Model evaluation with FiftyOne means slicing performance by class, scenario, and edge case with metrics such as precision, recall, mAP, and IoU, and using confusion matrices to see exactly where detection, segmentation, and vision-language models fail. These posts cover failure mode analysis and side-by-side model comparisons on real datasets.

Every model is a portrait of its dataset. We load open datasets such as COCO, LIBERO, and Open X-Embodiment into FiftyOne to explore what's really inside: the class imbalances, annotation errors, and hidden gems that summary statistics miss. Use these walkthroughs to evaluate a dataset before you train on it.

Plugins make FiftyOne fully extensible. These posts show you how to build custom panels, operators, and workflows on the FiftyOne plugin framework, and how to connect the tools already in your stack, from annotation platforms to vector databases and model APIs. If FiftyOne doesn't do it yet, a plugin probably can.

What's new at Voxel51: FiftyOne releases and features, partnerships, research from our ML team, and community events such as CVPR and our meetups. Start here to see where the FiftyOne platform is headed and what our team is building for physical AI and visual AI workflows.