Machine learningDeep learning / NLP / CV
Object Detection
Object detection is a computer vision task in which a deep neural network simultaneously locates and classifies every instance of one or more object categories within an image, producing a bounding box and a class label for each detected object. Modern detectors — from the R-CNN family to YOLO and DETR — achieve near-human accuracy at real-time speeds on standard benchmarks.
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Sources
- Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 580–587. DOI: 10.1109/CVPR.2014.81 ↗
- Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779–788. DOI: 10.1109/CVPR.2016.91 ↗
Related methods
Referenced by
Explainable Image ClassificationExplainable Object DetectionFine-Tuned Convolutional Neural NetworkFine-Tuned Image ClassificationImage ClassificationInstance SegmentationMultimodal Instance SegmentationMultimodal Object DetectionSelf-supervised Object DetectionSemantic SegmentationSemi-supervised Object DetectionTransfer Learning with Convolutional Neural NetworkTransfer Learning with Object DetectionWeakly Supervised Instance SegmentationWeakly Supervised Object DetectionWeakly Supervised Semantic Segmentation