Object Detection Part 4: Fast Detection Models
📰 Lilian Weng's Blog
One-stage object detection models like SSD, RetinaNet, and YOLO skip region proposal stage for fast detection
Action Steps
- Learn about the R-CNN family models
- Understand the concept of one-stage models
- Study SSD, RetinaNet, and YOLO models
- Apply one-stage models in object detection tasks
Who Needs to Know This
Computer vision engineers and researchers benefit from understanding one-stage models for efficient object detection, while software engineers can apply these models in real-world applications
Key Insight
💡 One-stage models directly apply detection on dense sampled areas for efficient object detection
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💡 One-stage models like YOLO and SSD enable fast object detection without region proposal stage
Key Takeaways
One-stage object detection models like SSD, RetinaNet, and YOLO skip region proposal stage for fast detection
Full Article
<!-- Part 4 of the "Object Detection for Dummies" series focuses on one-stage models for fast detection, including SSD, RetinaNet, and models in the YOLO family. These models skip the explicit region proposal stage but apply the detection directly on dense sampled areas. --> <p>In <a href="https://lilianweng.github.io/posts/2017-12-31-object-recognition-part-3/">Part 3</a>, we have reviewed models in the R-CNN family. All of them are region-based object detection algorithms. They can achieve hig
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