Deploy with Roboflow

MobileNet SSD v2

This architecture provides good realtime results on limited compute. It's designed to run in realtime (30 frames per second) even on mobile devices.

Try the Model

Use the widget below to experiment with MobileNet SSD v2. You can detect COCO classes such as people, vehicles, animals, household items.

Overview

What is MobileNetSSDv2?

MobileNetSSDv2 (MobileNet Single Shot Detector) is an object detection model with 267 layers and 15 million parameters. It provides real-time inference under compute constraints in devices like smartphones. Once trained, MobileNetSSDv2 can be stored with 63 MB, making it an ideal model to use on smaller devices.

MobileNetSSDv2 Architecture

The MobileNetSSDv2 Model essentially is a 2-part model. The first part consists of the base MobileNetV2 network with a SSD layer that classifies the detected image. In essence, the MobileNet base network acts as a feature extractor for the SSD layer which will then classify the object of interest.

Image in Courtesy of Matthijs Hollemans

MobileNetV2 Results

MobileNet V2 outperforms MobileNet V1 with higher accuracies and lower latencies.

Image courtesy of Google AI

MobileNet SSD v2 License

MobileNet SSD v2

is licensed under a

MIT

license.

Performance

Deploy a MobileNet SSD v2 API

You can use Roboflow Inference to deploy a

MobileNet SSD v2

API on your hardware. You can deploy the model on CPU (i.e. Raspberry Pi, AI PCs) and GPU devices (i.e. NVIDIA Jetson, NVIDIA T4).

Below are instructions on how to deploy your own model API.

Label Data Automatically with MobileNet SSD v2

You can automatically label a dataset using MobileNet SSD v2 with help from Autodistill, an open source package for training computer vision models. You can label a folder of images automatically with only a few lines of code. Below, see our tutorials that demonstrate how to use MobileNet SSD v2 to train a computer vision model.

No items found.