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We then augment SSD+Residual-101 with deconvolution layers to introduce additional large-scale context in object detection and improve accuracy, especially for small objects, calling our...

DSSD in pytorch This repository implements DSSD : Deconvolutional Single Shot Detector. The code were borrowed heavily from SSD. The things I did was the DSSD network definition, including the …

Jun 18, 2024 · In summary, the DSSD approach represents a milestone in the realm of object detection, offering a refined and potent methodology that not only boosts accuracy but also broadens the scope …

Jan 23, 2017 · We then augment SSD+Residual-101 with deconvolution layers to introduce additional large-scale context in object detection and improve accuracy, especially for small objects, calling our …

Mar 7, 2024 · Our new DSSD model is able to outperform the previous SSD framework, especially on small object or context specific objects, while still preserving comparable speed to other detectors.

The DSSD 321 model maintains a speed advantage over R-FCN, but with a small drop in ac-curacy. Our proposed DSSD model achieves state-of-the-art accuracy while maintaining a reasonable speed …

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Jan 23, 2017 · Figure 1: Networks of SSD and DSSD on residual network. The blue modules are the layers added in SSD framew ork, and we call them SSD Layers. In the bottom figure, the red layers …

Jun 9, 2021 · DSSD requires custom bounding box parsers that are not built-in inside the DeepStream SDK. The source code to build custom bounding box parsers for DSSD is available here.