How long does it take to train object detection?
Answers from top 12 papers
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| Papers (12) | Insight |
|---|---|
18 Citations | Experimental results prove that the proposed end-to-end network outperforms the state-of-the-art object detection approaches, and the learned discriminative features can effectively capture the coexistence relations to improve classification performance of multi-class objects in the object detection task. |
07 Oct 2001 16 Citations | Experiments show an improvement in object detection accuracy and a 78% reduction in detection time. |
06 Jul 2003 | Experimental results show that the spatial context models improve the accuracy of natural object detection by 13% over the individual object detectors themselves. |
13 Jul 2015 | For the object classes car, pedestrian and bicyclist the resulting detector achieves best-in-class detection and timing performance relative to prior art on the KITTI dataset as well as compared to another existing 3D object detection approach. |
| Object detection includes object localization and object classification. | |
| It moves further towards fully learnable object detection. | |
| Experimental results show that our method is effective which leads to state-of-the-art performance for object detection. | |
07 Jun 2015 | Our approach achieves the state-of-the-art performance for both object proposal and object detection tasks, comparing to previous object proposal methods. |
37 Citations | Experimental results on a publicly available ten-class object detection data set demonstrate the superiority of the RECNN over several competitive object detection methods. |
06 May 2013 | The results suggest a significant improvement over static object detection. |
07 Dec 2015 | It also shows performance competitive with the state-of-the-art methods for pedestrian detection and general object detection tasks. |
27 Dec 2005 27 Citations | The experimental results show that the proposed approaches are efficient in object detection. |
Related Questions
How to train object detection model?5 answersTo train an object detection model effectively, one can utilize dataset-agnostic templates like VFNet, ATSS, and SSD, which provide a strong baseline across various datasets. Additionally, self-supervised pretraining methods, such as spatially consistent dense representation learning and masked image modeling, can enhance the model's ability to capture task-agnostic representations and improve downstream fine-tuning performance. Human-annotated ground truth data plays a crucial role in training accurate models, with frameworks like OpenCV-based deep learning techniques aiding in assigning identities to objects for robust training datasets. By incorporating these approaches, one can optimize the training process and achieve high accuracy in object detection models.
What is the latest algorithm deep learning for object detection?3 answersThe latest algorithm for deep learning object detection is the Single Shot Detector (SSD) algorithm. SSD is an improved detection algorithm that uses the lightweight network MobileNet as the basic network in the SSD model to reduce computation time and extract image features efficiently. It establishes correlations between frames and achieves good performance in terms of detection accuracy and speed. Other popular algorithms for object detection include Faster R-CNN, HOG, R-CNN, R-FCN, SPP-net, and YOLO versions. These algorithms have been widely studied and compared through sample experiments. Real-time object detection using deep learning has become a burgeoning field, with CNNs being trained on large-scale image datasets to extract discriminative features effectively. Faster CNN architectures like SSD and YOLO, along with hardware acceleration strategies, have been proposed to improve real-time performance.
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