This architecture provides good realtime results on limited compute. It's designed to run in realtime (30 frames per second) even on mobile devices.
Here is an overview of the
model:
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.
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
MobileNet V2 outperforms MobileNet V1 with higher accuracies and lower latencies.
Image courtesy of Google AI
Training a TensorFlow MobileNet Object Detection Model with a Custom Dataset: https://blog.roboflow.ai/training-a-tensorflow-object-detection-model-with-a-custom-dataset/
YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.
YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.
YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.
YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.
MobileNet SSD v2
uses the
Tensorflow TFRecord
annotation format. If your annotation is in a different format, you can use Roboflow's annotation conversion tools to get your data into the right format.
Curious about how YOLOv5 compares to other models? Check out our model comparisons.
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