Our new model ZOO works with DepthAI V3. Find out more in our documentation.
Model Details
Model Description
MobileObjectLocalizer is a general-purpose object detection model developed by Google that can be used for any type of object. Unlike models such as YOLO which classifies objects among the predefined set of classes, MobileObjectLocalizer can detect any object. and does not assign any category.
Developed by: Google
Shared by:
Model type: Computer vision
License:
Training Details
No training details available.
Testing Details
No testing details available.
Technical Specifications
Input/Output Details
Input:
Name: normalized_input_image_tensor
Info: NCHW BGR un-normalized image
Output:
Name: scores and bboxes
Info: Scores and bounding boxes that still need NMS.
Model Architecture
Backbone: MobileNetV2 backbone with a 0.75 width-multiplier,
* Benchmarked with , using 2 threads (and the DSP runtime in balanced mode for RVC4).
* Parameters and FLOPs are obtained from the package.
Utilization
Models converted for RVC Platforms can be used for inference on OAK devices.
DepthAI pipelines are used to define the information flow linking the device, inference model, and the output parser (as defined in model head(s)).
Below, we present the most crucial utilization steps for the particular model.
Please consult the docs for more information.
DetectionParser that outputs message with bounding boxes and scores.
Get parsed output(s):
while pipeline.isRuning():
parser_output: ImgDetectionsExtended = parser_output_queue.get()
Example
You can quickly run the model using our script.
It automatically downloads the model, creates a DepthAI pipeline, runs the inference, and displays the results using our DepthAI visualizer tool.
To try it out, run:
