Our new model ZOO works with DepthAI V3. Find out more in our documentation.
Model Details
Model Description
YOLOv8 Instance Segmentation is a YOLOv8 based convolutional neural network model designed for identifying individual objects in an image and segmenting them from the rest of the image. It is highly effective and accurate even for more tricky images.
We implement here the nano version of the model.
Developed by: Ultralytics
Shared by: Luxonis
Model type: Computer Vision
License:
Resources for more information:
Training Details
Training Data
The model was trained on a private dataset that combines the public and datasets from Roboflow with additional, proprietary synthetic data.
Testing Details
Metrics
Results of the mAP (50-95) are evaluated on the above private dataset with the input resolution of 640×640.
Model
mAP (box)
mAP (mask)
YOLOv8n-seg-box
86.9
82.8
Technical Specifications
Input/Output Details
Input:
Name: image
Info: NCHW BGR un-normalized image
Outputs:
Name: Multiple (please consult NN archive config.json)
Info: Unprocessed outputs of a multitude of detections, masks and protos
Model Architecture
Backbone: CSPDarknet53
Head: Anchor-free object segmentation head (pruned of concatenation)
Consult the for more information.
Throughput
Model variant: yolov8-instance-segmentation-nano-box:512x320
* 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.
YOLOExtendedParser that outputs message (bounding boxes and segmentation masks of detected objects).
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:
