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SSD (Single Shot MultiBox Detector)

Rating:

4.7 / 5.0

Tags

Computer Vision, Object Detection, Real-time, Deep Learning, Algorithm, Open Source

Categories:
Computer visionMachine learning and neural networks
Creator Различные исследователи
Date 2015-12-01
Platforms Frameworks
Status Active (Development)
Website github.com
Price Model Free (model)
Sections:
DL FrameworksImage AnalysisObject DetectionObject RecognitionVideo Analysis

Pricing Details

Free (open source). Costs may apply when using third-party platforms or cloud services that provide access to SSD models.

Features

Object detection, high speed, single-pass detection, multi-scale feature maps, anchor boxes, open-source.

Integrations

Integrates as a model or library into projects in Python, C++, and other languages using frameworks (TensorFlow, PyTorch, Caffe).

Preview

SSD (Single Shot MultiBox Detector) is an object detection algorithm that, similar to YOLO, performs detection in a single pass of a neural network, ensuring high speed. Unlike two-stage methods that first propose regions of interest and then classify them, SSD predicts bounding boxes and class probabilities directly from a set of predefined bounding boxes (anchors) at various scales. Using multiple layers for predictions at different resolution levels allows SSD to effectively detect objects of various sizes. SSD is open-source and widely used in tasks requiring fast and accurate object detection.