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Intermediate. Assumes working knowledge.
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AI and machine learningProgramming languagesElixir and ErlangComputer visionNeural networks and deep learning
This video demonstrates the new features in Elixir YOLO v0.2.0, including YOLOX support, custom models, and Nx.Defn performance boosts.
🎉 YOLO Elixir v0.2.0 is out! This release brings three key enhancements that significantly expand what you can do with object detection in Elixir. In this video, I walk you through what’s new: YOLOX Support Support for YOLOX models (Apache 2.0 license!) brings more flexibility and fewer licensing restrictions compared to Ultralytics models. Model-Agnostic Postprocessing No more fixed output shapes. You can now use models trained on your own dataset, with any number of classes! Performance Boost Postprocessing now runs ~100x faster thanks to Nx.Defn — real-time capable even without FastNMS. Plus: Updated docs & LiveBooks, FastNMS 0.2.0 (now model-agnostic) Previews of what’s coming next. 📚 GitHub repo, code samples, and benchmarks: github.com/poeticoding/yolo_elixir Article: poeticoding.com/elixir-yolo-v0-2-0-yolox-support-custom-mode… Ultralytics License page: ultralytics.com/license YOLOX github repo: github.com/Megvii-BaseDetection/YOLOX Ultralytics Open Images V7 models: docs.ultralytics.com/datasets/detect/open-images-v7/ Intro YOLOX Support Model-agnostic postprocessing Performance Boost What's NextWhat people say. Shared to explain the capabilities of the latest Elixir YOLO library version for domain-specific object detection.
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