Object detection models fall into two main categories: two-stage and one-stage models. Two-stage models, like Faster R-CNN, first generate region proposals and then classify these proposals into objects. This approach is known for its high accuracy but can be slower due to the extra processing step. Faster R-CNN is a common choice for tasks that require precise object localization. Another two-stage model, R-FCN (Region-based Fully Convolutional Networks), offers better speed by making the region proposal process more efficient. One-stage models, such as YOLO (You Only Look Once) and SSD (Single Shot Multibox Detector), are designed to be faster by directly predicting bounding boxes and class labels from the entire image in one pass. YOLO is known for its speed, making it ideal for real-time applications such as video surveillance or autonomous driving. SSD, like YOLO, is designed for real-time processing but offers better accuracy at the cost of slightly reduced speed. Other recent one-stage models, such as EfficientDet, aim to balance speed and accuracy, achieving high performance on resource-constrained devices. There are also transformer-based models like DETR (Detection Transformer), which treat object detection as a direct set prediction problem. While these models are relatively new, they have shown promise in improving accuracy and robustness, especially in complex scenes with multiple objects.
What are the different types of object detection models?
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