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 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.

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 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 …

This paper presents the multi-task Deconvolutional Single Shot Detector (MT-DSSD), which runs three tasks-object detection, semantic object segmentation, and gr

Aug 25, 2018 · DSSD utilises the generated feature maps from deconvolutional modules in order to make use of the increased resolution. Prediction Modules are thus attached after Deconvolution …

Feb 18, 2020 · DSSD uses the learned deconvolution layers instead of bilinear up-sampling and adds a batch normalization layer after each convolution layer. In addition, DSSD adopts element-wise …

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 …