The mobilenet-ssd model is a Single-Shot multibox Detection (SSD) network intended to perform object detection. This model is implemented using the Caffe* framework. For details about this model, check out the repository.
What can MobileNet SSD detect?
Object Detection using SSD Mobilenet and Tensorflow Object Detection API : Can detect any single class from coco dataset. SSD (Single Shot MultiBox Detector) is a popular algorithm in object detection. It’s generally faster than Faster RCNN.
What is MobilenetSSD?
MobilenetSSD is an object detection model that computes the bounding box and category of an object from an input image. This Single Shot Detector (SSD) object detection model uses Mobilenet as backbone and can achieve fast object detection optimized for mobile devices.
Which is better Yolo or SSD?
Base network and detection network. SSDs, RCNN, Faster RCNN, etc are examples of detection networks.
Difference between SSD & YOLO.
|When the object size is tiny, the performance dips a touch||YOLO could be a higher choice even when the object size is small.|
Why is MobileNet SSD used?
Use Case and High-Level Description
The mobilenet-ssd model is a Single-Shot multibox Detection (SSD) network intended to perform object detection. This model is implemented using the Caffe* framework.
How do I train my SSD Caffe?
Train your own dataset
- Create the labelmap. …
- Use gen_model.sh to generate your own training prototxt.
- Download the training weights from the link above, and run train.sh, after about 30000 iterations, the loss should be 1.5 – 2.5.
- Run test.sh to evaluate the result.