Model Overview

With a ResNet-50 backbone and a number of architectural modifications, this version provides better accuracy and performance.

Model Architecture

Despite the changes described in the previous section, the overall architecture, as described in the following diagram, has not changed.

Figure 1. The architecture of a Single Shot MultiBox Detector model. Image has been taken from the Single Shot MultiBox Detector paper.

The backbone is followed by 5 additional convolutional layers. In addition to the convolutional layers, we attached 6 detection heads:

• The first detection head is attached to the last conv4_x layer.
• The other five detection heads are attached to the corresponding 5 additional layers.

Training

This model was trained using script available on NGC and in GitHub repo.

Dataset

The following datasets were used to train this model:

• COCO 2017 - Dataset for large-scale object detection, segmentation and captioning.

Performance

Performance numbers for this model are available in NGC.

References

• Original paper
• NVIDIA model implementation in NGC
• NVIDIA model implementation on GitHub

License

This model was trained using open-source software available in Deep Learning Examples repository. For terms of use, please refer to the license of the script and the datasets the model was derived from.