This section provides examples to help you get started with AI model inference using DepthAI. The examples utilize publicly available models from the Model ZOO and can be run on a Luxonis device of choice. The inference is made either on camera inputs or custom media files (image or video) and the results are displayed in the browser.
If not explicitly marked or specified examples can run both on RVC2 and RVC4 devices. Furthermore, RVC4 has the ability to run the example in the peripheral or standalone mode. Standalone refers to a mode where the whole application and data are first copied from host to device and then everything is executed on the device, without the need for a host computer. In contrast, peripheral mode requires a direct connection to a host computer and there is device-to-host (or vice versa) communication which can cause lower FPS. You can read more about the setup process and software details here.
- Generic example
- This is a special case example. It is not task-dependent and can run a generic inference pipeline for a single model with a single-image input and a single-head output. Its purpose is a quick visualization of simpler models without any additional postprocessing logic. It can serve as a starting point for more complex use cases.
- Classification
- Object Detection
- Face Detection
- 3D Detection
- Keypoint Detection
- Pose Estimation
- Segmentation
- Object Tracking
- Counting
- OCR
- Reidentification
- Depth Estimation
- Line Detection
- Image-to-Image Translation
- Feature Detection
- Speech Recognition
LEGEND: ✅: available; ❌: not available; 🚧: work in progress
EfficientNet-lite | Image Quality Assessment |
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Name | HubAI Model | RVC2 | RVC4 (peripheral) | RVC4 (standalone) | DepthAIv2 | Notes |
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generic-example | efficientnet-lite | ✅ | ✅ | ✅ | gen2-efficientnet-classification | |
generic-example | efficientvit | ✅ | ✅ | ✅ | ||
generic-example | image-quality-assessment | ✅ | ❌ | ❌ | gen2-image-quality-assessment | Model for RVC4 is not yet available. |
YoloV6 Nano | YoloP |
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Blur Faces | Emotion Recognition |
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Name | HubAI Model | RVC2 | RVC4 (peripheral) | RVC4 (standalone) | DepthAIv2 | Notes |
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generic-example | yunet | ✅ | ✅ | ✅ | gen2-face-detection | |
generic-example | scrfd-face-detection | ✅ | ✅ | ✅ | gen2-face-detection | |
age-gender | yunet, age-gender-recognition | ✅ | ✅ | ✅ | gen2-age-gender | |
blur-faces | yunet | ✅ | ✅ | ✅ | gen2-blur-faces | |
emotion-recognition | yunet, emotion-recognition | ✅ | ✅ | ✅ | gen2-emotion-recognition | |
fatigue-detection | yunet | ✅ | ✅ | ✅ | gen2-fatigue-detection | |
gaze-estimation | yunet, gaze-estimation-adas | ✅ | ✅ | ✅ | gen2-gaze-estimation | Only works with camera input (--media flag not supported). |
head-posture-detection | yunet, l2cs-net | ✅ | ✅ | ✅ | gen2-head-posture-detection | |
face-mask-detection | ppe-detection | ✅ | ✅ | ✅ | gen2-mask-detection |
Objectron |
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Model | RVC2 | RVC4 (peripheral) | RVC4 (standalone) | DepthAIv2 | Notes |
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objectron | yolov6-nano, objectron | ✅ | ✅ | ✅ | gen2-objectron |
Mediapipe Face Landmarker |
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Name | HubAI Model | RVC2 | RVC4 (peripheral) | RVC4 (standalone) | DepthAIv2 | Notes |
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generic-example | mediapipe-face-landmarker | ✅ | ✅ | ✅ | gen2-facemesh |
Human Pose | Animal Pose |
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Name | HubAI Model | RVC2 | RVC4 (peripheral) | RVC4 (standalone) | DepthAIv2 | Notes |
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generic-example | yolov8-nano-pose-estimation / yolov8-large-pose-estimation | ✅ | ✅ | ✅ | gen2-human-pose | |
human-pose | yolov6-nano, lite-hrnet | ✅ | ✅ | ✅ | gen2-human-pose | |
animal-pose | yolov6-nano, superanimal-landmarker | ✅ | ✅ | ✅ | ||
hand-pose | mediapipe-palm-detection, mediapipe-hand-landmarker | ✅ | ✅ | ✅ |
YoloV8 Instance Segmentation Nano | FastSam S |
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Name | HubAI Model | RVC2 | RVC4 (peripheral) | RVC4 (standalone) | DepthAIv2 | Notes |
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generic-example | yolov8-instance-segmentation-nano / yolov8-instance-segmentation-large | ✅ | ✅ | ✅ | gen2-maskrcnn-resnet50, gen2-deeplabv3_multiclass | |
generic-example | mediapipe-selfie-segmentation | ✅ | ✅ | ✅ | gen2-deeplabv3_person | |
generic-example | deeplab-v3-plus | ✅ | ✅ | ✅ | gen2-deeplabv3_multiclass | |
generic-example | fastsam-s | ✅ | ✅ | ✅ | ||
generic-example | fastsam-x | ❌ | ✅ | ✅ | Model is only available for RVC4. | |
generic-example | ewasr | ✅ | ✅ | ✅ | ||
generic-example | pp-liteseg | ✅ | ✅ | ✅ | gen2-road-segmentation | |
depth-crop | deeplab-v3-plus | ✅ | ✅ | ✅ | gen2-deeplabv3-depth | |
background blur | deeplab-v3-plus | ✅ | ✅ | ✅ |
DeepSORT Tracking | People Tracker |
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Name | HubAI Model | RVC2 | RVC4 (peripheral) | RVC4 (standalone) | DepthAIv2 | Notes |
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kalman | yolov6-nano | ✅ | 🚧 | 🚧 | gen2-bounding-box-filtering/kalman | Needs a device with color and stereo (left, right) cameras. |
collision-avoidance | yolov6-nano | ✅ | 🚧 | 🚧 | gen2-collision-avoidance | Needs a device with color and stereo (left, right) cameras. |
deepsort-tracking | yolov6-nano, OSNet | ✅ | ✅ | ✅ | gen2-deepsort-tracking | |
people-tracker | scrfd-person-detection | ✅ | 🚧 | 🚧 | gen2-people-tracker |
Crowd Counting | People Counter |
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Name | HubAI Model | RVC2 | RVC4 (peripheral) | RVC4 (standalone) | DepthAIv2 | Notes |
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crowd-counting | dm-count | ✅ | ✅ | ✅ | gen2-crowdcounting | |
cumulative-object-counting | Mobilenet-SSD | ✅ | ❌ | ❌ | gen2-cumulative-object-counting | Model is only available for RVC2. |
depth-people-counting | 🚧 | 🚧 | 🚧 | gen2-depth-people-counting | example does not use any NN model | |
people-counter | scrfd-person-detection | ✅ | ✅ | ✅ | gen2-people-counter |
License Plate Recognition |
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Name | HubAI Model | RVC2 | RVC4 (peripheral) | RVC4 (standalone) | DepthAIv2 | Notes |
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license-plate-recognition | yolov6-nano, license-plate-detection, paddle-text-recognition | ❌ | ✅ | ✅ | gen2-license-plate-recognition | Due to the high computational cost, this example only works on RVC4 devices. |
general-ocr | paddle-text-detection, paddle-text-recognition | ✅ | ✅ | ✅ | gen2-ocr, gen2-seven-segment-recognition |
Human Reidentification (Pose) | Human Reidentification (Face) |
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Name | HubAI Model | RVC2 | RVC4 (peripheral) | RVC4 (standalone) | DepthAIv2 | Notes |
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human-reidentification | scrfd-person-detection/scrfd-face-detection/yunet, osnet/arcface | ✅ | ✅ | ✅ | gen2-pedestrian-reidentification, gen2-face-recognition |
CREStereo Stereo Matching | MiDaS v2.1 |
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Name | HubAI Model | RVC2 | RVC4 (peripheral) | RVC4 (standalone) | DepthAIv2 | Notes |
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crestereo-stereo-matching | crestereo | ✅ | 🚧 | 🚧 | gen2-crestereo-stereo-matching | |
generic-example | midas-v2-1 | ✅ | ✅ | ✅ | gen2-depth-mbnv2, gen2-fast-depth, gen2-mega-depth | |
generic-example | depth-anything-v2 | ❌ | ✅ | ✅ | Model is only available for RVC4. |
M-LSD | Ultra Fast Lane Detection |
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Name | HubAI Model | RVC2 | RVC4 (peripheral) | RVC4 (standalone) | DepthAIv2 | Notes |
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generic-example | m-lsd | ✅ | ✅ | ✅ | ||
generic-example | m-lsd-tiny | ✅ | ❌ | ❌ | Model is only available for RVC2. | |
generic-example | ultra-fast-lane-detection | ✅ | ✅ | ✅ | gen2-lanenet |
Name | HubAI Model | RVC2 | RVC4 (peripheral) | RVC4 (standalone) | DepthAIv2 | Notes |
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generic-example | zero-dce | ✅ | ✅ | ✅ | ||
generic-example | rt-super-resolution | ✅ | ✅ | ✅ | ||
generic-example | esrgan | ❌ | ✅ | ✅ | Missing visualizations. | |
generic-example | dncnn3 | ✅ | ✅ | ✅ | Missing visualization, model needs grayscale input. |
XFeat |
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Name | HubAI Model | RVC2 | RVC4 (peripheral) | RVC4 (standalone) | DepthAIv2 | Notes |
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xfeat | xfeat | ✅ | ✅ | ✅ | Needs a device with stereo (left and right) cameras. |
Speech Recognition |
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Name | HubAI Model | RVC2 | RVC4 (peripheral) | RVC4 (standalone) | DepthAIv2 | Notes |
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whisper-tiny-en | whisper-tiny-en | ❌ | ✅ | ✅ | Model is only available for RVC4 |