AI / ML / NN
OAK cameras can run any AI model, even custom
architectured/built ones. You can even run multiple AI models at the same time, either in parallel or series (a demo here).
AI vision tasks
We have open-source examples and demos for many different AI vision tasks, such as:
Object detection models provide bounding box, confidence, and label of all detected objects. Demos: MobileNet, Yolo, EfficientDet, Palm detection.
Landmark detection models provide landmarks/keypoints of an object. Demos: Human pose, hand landmarks, and facial landmarks.
Semantic segmentation models provide label/class for each pixel. Demos: Person segmentation, multiclass segmentation, road segmentation.
Classification models provide classification label and confidence in that label. Demos: EfficientNet, Tensorflow classification, fire classification, emotions classification.
Recognition models provide byte array that can be used for recognition or recognized feature itself. Demos: Face recognition, person identification, OCR, license plate recognition.
There are also many other AI vision tasks that don’t fall in any of the categories above, like crowd counting,
monocular depth estimation, gaze estimation, or
All of the demos above run on color/grayscale frames. Many of these vision tasks can be fused with the depth perception
(on the OAK camera itself), which unlocks the power of Spatial AI.
AI model performance depends on the accelerator that’s on the OAK device. For current devices that use
RVC2 you can find the performance results here.
Head over to Discussion Forum
for technical support or any other questions you might have.