GstInference GStreamer pipelines for Jetson TX2

GstInference
Introduction
Getting started
Getting the code Building the plugin Quick Starting Guide
Supported architectures
InceptionV1 InceptionV2 InceptionV3 InceptionV4 TinyYoloV2 TinyYoloV3 YoloV2 ResNetV1 MobileNetV2 MobileNetV2 + SSD AlexNet
Supported backends
TensorFlow Tensorflow-Lite Coral from Google Caffe TensorRT ONNXRT ONNXRT ACL ONNXRT OpenVINO
Metadata and Signals
GstClassificationMeta GstInferenceMeta Signals
Overlay Elements
Inference Overlay
Utils Elements
InferenceFilter InferenceCrop InferenceDebug InferenceBin
Legacy pipelines
PC NANO TX2 Xavier IMX8
Example pipelines
Example applications
Classification Detection Hand Pose Recognition
Benchmarks
Model Zoo
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Releases Roadmap
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Problems running the pipelines shown on this page? Please see our GStreamer Debugging guide for help.

Tensorflow

Inceptionv4 inference on image file using Tensorflow

• Get the graph used on this example from this link
• You will need an image file from one of ImageNet classes
• Pipeline

IMAGE_FILE=cat.jpg
MODEL_LOCATION='graph_inceptionv4_tensorflow.pb'
INPUT_LAYER='input'
OUTPUT_LAYER='InceptionV4/Logits/Predictions'
GST_DEBUG=inceptionv4:6 gst-launch-1.0 \
multifilesrc location=$IMAGE_FILE ! jpegparse ! nvjpegdec ! 'video/x-raw' ! nvvidconv ! 'video/x-raw(memory:NVMM),format=NV12' ! nvvidconv ! queue ! net.sink_model \
inceptionv4 name=net model-location=$MODEL_LOCATION backend=tensorflow backend::input-layer=$INPUT_LAYER  backend::output-layer=$OUTPUT_LAYER -

• Output

0:02:22.005527960 30355       0x5accf0 LOG              inceptionv4 gstinceptionv4.c:200:gst_inceptionv4_preprocess:<net> Preprocess
0:02:22.168796723 30355       0x5accf0 LOG              inceptionv4 gstinceptionv4.c:232:gst_inceptionv4_postprocess:<net> Postprocess
0:02:22.168947603 30355       0x5accf0 LOG              inceptionv4 gstinceptionv4.c:253:gst_inceptionv4_postprocess:<net> Highest probability is label 282 : (0.627314)
0:02:22.169237202 30355       0x5accf0 LOG              inceptionv4 gstinceptionv4.c:200:gst_inceptionv4_preprocess:<net> Preprocess
0:02:22.339393463 30355       0x5accf0 LOG              inceptionv4 gstinceptionv4.c:232:gst_inceptionv4_postprocess:<net> Postprocess
0:02:22.339496918 30355       0x5accf0 LOG              inceptionv4 gstinceptionv4.c:253:gst_inceptionv4_postprocess:<net> Highest probability is label 282 : (0.627314)
0:02:22.339701878 30355       0x5accf0 LOG              inceptionv4 gstinceptionv4.c:200:gst_inceptionv4_preprocess:<net> Preprocess
0:02:22.507804674 30355       0x5accf0 LOG              inceptionv4 gstinceptionv4.c:232:gst_inceptionv4_postprocess:<net> Postprocess
0:02:22.507950081 30355       0x5accf0 LOG              inceptionv4 gstinceptionv4.c:253:gst_inceptionv4_postprocess:<net> Highest probability is label 282 : (0.627314)
0:02:22.508232128 30355       0x5accf0 LOG              inceptionv4 gstinceptionv4.c:200:gst_inceptionv4_preprocess:<net> Preprocess
0:02:22.678740356 30355       0x5accf0 LOG              inceptionv4 gstinceptionv4.c:232:gst_inceptionv4_postprocess:<net> Postprocess
0:02:22.678892356 30355       0x5accf0 LOG              inceptionv4 gstinceptionv4.c:253:gst_inceptionv4_postprocess:<net> Highest probability is label 282 : (0.627314)

Inceptionv4 inference on video file using TensorFlow

• Get the graph used on this example from this link
• You will need a video file from one of ImageNet classes
• Pipeline

VIDEO_FILE='cat.mp4'
MODEL_LOCATION='graph_inceptionv4_tensorflow.pb'
INPUT_LAYER='input'
OUTPUT_LAYER='InceptionV4/Logits/Predictions'
GST_DEBUG=inceptionv4:6 gst-launch-1.0 \
filesrc location=$VIDEO_FILE ! qtdemux name=demux ! h264parse ! omxh264dec ! nvvidconv ! queue ! net.sink_model \
inceptionv4 name=net model-location=$MODEL_LOCATION backend=tensorflow backend::input-layer=$INPUT_LAYER  backend::output-layer=$OUTPUT_LAYER

• Output

0:00:11.728307018 30399       0x5ad000 LOG              inceptionv4 gstinceptionv4.c:200:gst_inceptionv4_preprocess:<net> Preprocess
0:00:11.892030154 30399       0x5ad000 LOG              inceptionv4 gstinceptionv4.c:232:gst_inceptionv4_postprocess:<net> Postprocess
0:00:11.892258185 30399       0x5ad000 LOG              inceptionv4 gstinceptionv4.c:253:gst_inceptionv4_postprocess:<net> Highest probability is label 282 : (0.686857)
0:00:11.892556808 30399       0x5ad000 LOG              inceptionv4 gstinceptionv4.c:200:gst_inceptionv4_preprocess:<net> Preprocess
0:00:12.065318539 30399       0x5ad000 LOG              inceptionv4 gstinceptionv4.c:232:gst_inceptionv4_postprocess:<net> Postprocess
0:00:12.065467786 30399       0x5ad000 LOG              inceptionv4 gstinceptionv4.c:253:gst_inceptionv4_postprocess:<net> Highest probability is label 282 : (0.673300)
0:00:12.065759849 30399       0x5ad000 LOG              inceptionv4 gstinceptionv4.c:200:gst_inceptionv4_preprocess:<net> Preprocess
0:00:12.247159695 30399       0x5ad000 LOG              inceptionv4 gstinceptionv4.c:232:gst_inceptionv4_postprocess:<net> Postprocess
0:00:12.247309295 30399       0x5ad000 LOG              inceptionv4 gstinceptionv4.c:253:gst_inceptionv4_postprocess:<net> Highest probability is label 282 : (0.669102)
0:00:12.247612718 30399       0x5ad000 LOG              inceptionv4 gstinceptionv4.c:200:gst_inceptionv4_preprocess:<net> Preprocess
0:00:12.419172436 30399       0x5ad000 LOG              inceptionv4 gstinceptionv4.c:232:gst_inceptionv4_postprocess:<net> Postprocess
0:00:12.419321396 30399       0x5ad000 LOG              inceptionv4 gstinceptionv4.c:253:gst_inceptionv4_postprocess:<net> Highest probability is label 282 : (0.667991)

Inceptionv4 inference on camera stream using TensorFlow

• Get the graph used on this example from this link
• You will need a camera compatible with Nvidia Libargus API or V4l2.

Nvidia Camera

• Pipeline

SENSOR_ID=0
MODEL_LOCATION='graph_inceptionv4_tensorflow.pb'
INPUT_LAYER='input'
OUTPUT_LAYER='InceptionV4/Logits/Predictions'
GST_DEBUG=inceptionv4:6 gst-launch-1.0 \
nvcamerasrc sensor-id=$SENSOR_ID ! nvvidconv ! queue ! net.sink_model \
inceptionv4 name=net backend=tensorflow model-location=$MODEL_LOCATION backend::input-layer=$INPUT_LAYER backend::output-layer=$OUTPUT_LAYER

V4L2

• Pipeline

CAMERA='/dev/video1'
MODEL_LOCATION='graph_inceptionv4_tensorflow.pb'
INPUT_LAYER='input'
OUTPUT_LAYER='InceptionV4/Logits/Predictions'
GST_DEBUG=inceptionv4:6 gst-launch-1.0 \
v4l2src device=$CAMERA ! videoconvert ! videoscale ! queue ! net.sink_model \
inceptionv4 name=net model-location=$MODEL_LOCATION backend=tensorflow backend::input-layer=$INPUT_LAYER  backend::output-layer=$OUTPUT_LAYER

• Output

0:00:12.199657219  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:200:gst_inceptionv4_preprocess:<net> Preprocess
0:00:12.365172092  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:232:gst_inceptionv4_postprocess:<net> Postprocess
0:00:12.365271548  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:253:gst_inceptionv4_postprocess:<net> Highest probability is label 774 : (0.196048)
0:00:12.365421435  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:200:gst_inceptionv4_preprocess:<net> Preprocess
0:00:12.530604726  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:232:gst_inceptionv4_postprocess:<net> Postprocess
0:00:12.530700501  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:253:gst_inceptionv4_postprocess:<net> Highest probability is label 774 : (0.179406)
0:00:12.530848565  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:200:gst_inceptionv4_preprocess:<net> Preprocess
0:00:12.697053611  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:232:gst_inceptionv4_postprocess:<net> Postprocess
0:00:12.697147818  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:253:gst_inceptionv4_postprocess:<net> Highest probability is label 774 : (0.144033)
0:00:12.697295530  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:200:gst_inceptionv4_preprocess:<net> Preprocess
0:00:12.862007878  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:232:gst_inceptionv4_postprocess:<net> Postprocess
0:00:12.862104134  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:253:gst_inceptionv4_postprocess:<net> Highest probability is label 774 : (0.157707)
0:00:12.862252645  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:200:gst_inceptionv4_preprocess:<net> Preprocess
0:00:13.027090881  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:232:gst_inceptionv4_postprocess:<net> Postprocess
0:00:13.027190273  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:253:gst_inceptionv4_postprocess:<net> Highest probability is label 774 : (0.142998)

Inceptionv4 visualization with classification overlay Tensorflow

• Get the graph used on this example from this link
• You will need a camera compatible with Nvidia Libargus API or V4l2.

Nvidia Camera

• Pipeline

SENSOR_ID=0
MODEL_LOCATION='graph_inceptionv4_tensorflow.pb'
INPUT_LAYER='input'
OUTPUT_LAYER='InceptionV4/Logits/Predictions'
LABELS='imagenet_labels.txt'
gst-launch-1.0 \
nvcamerasrc sensor-id=$SENSOR_ID ! 'video/x-raw(memory:NVMM)' ! tee name=t \
t. ! queue max-size-buffers=1 leaky=downstream ! nvvidconv ! 'video/x-raw,format=(string)RGBA' ! net.sink_model \
t. ! queue max-size-buffers=1 leaky=downstream ! nvvidconv ! 'video/x-raw,format=(string)RGBA' ! net.sink_bypass \
inceptionv4 name=net backend=tensorflow model-location=$MODEL_LOCATION backend::input-layer=$INPUT_LAYER backend::output-layer=$OUTPUT_LAYER \
net.src_bypass ! classificationoverlay labels="$(cat $LABELS)" font-scale=4 thickness=4 ! nvvidconv ! nvoverlaysink sync=false -v

V4L2

• Pipeline

CAMERA='/dev/video1'
MODEL_LOCATION='graph_inceptionv4_tensorflow.pb'
INPUT_LAYER='input'
OUTPUT_LAYER='InceptionV4/Logits/Predictions'
LABELS='imagenet_labels.txt'
gst-launch-1.0 \
v4l2src device=$CAMERA ! "video/x-raw, width=1280, height=720" ! tee name=t \
t. ! videoconvert ! videoscale ! queue ! net.sink_model \
t. ! queue ! net.sink_bypass \
inceptionv4 name=net model-location=$MODEL_LOCATION backend=tensorflow backend::input-layer=$INPUT_LAYER  backend::output-layer=$OUTPUT_LAYER \
net.src_bypass ! videoconvert ! classificationoverlay labels="$(cat $LABELS)" font-scale=4 thickness=4 ! videoconvert ! xvimagesink sync=false

• Output

Example classification overlay output

TinyYolov2 inference on image file using Tensorflow

• Get the graph used on this example from this link
• You will need an image file from one of TinyYOLO classes
• Pipeline

IMAGE_FILE='cat.jpg'
MODEL_LOCATION='graph_tinyyolov2_tensorflow.pb'
INPUT_LAYER='input/Placeholder'
OUTPUT_LAYER='add_8'
GST_DEBUG=tinyyolov2:6 gst-launch-1.0 \
multifilesrc location=$IMAGE_FILE ! jpegparse ! nvjpegdec ! 'video/x-raw' ! nvvidconv ! 'video/x-raw(memory:NVMM),format=NV12' ! nvvidconv ! queue ! net.sink_model \
tinyyolov2 name=net model-location=$MODEL_LOCATION backend=tensorflow backend::input-layer=$INPUT_LAYER  backend::output-layer=$OUTPUT_LAYER

• Output

0:00:07.137677204 30513       0x5accf0 LOG               tinyyolov2 gsttinyyolov2.c:479:gst_tinyyolov2_preprocess:<net> Preprocess
0:00:07.266928985 30513       0x5accf0 LOG               tinyyolov2 gsttinyyolov2.c:501:gst_tinyyolov2_postprocess:<net> Postprocess
0:00:07.267080761 30513       0x5accf0 LOG               tinyyolov2 gsttinyyolov2.c:384:print_top_predictions:<net> Box: [class:7, x:25.820670, y:11.977936, width:425.495203, height:450.224357, prob:15.204609]
0:00:07.267382968 30513       0x5accf0 LOG               tinyyolov2 gsttinyyolov2.c:479:gst_tinyyolov2_preprocess:<net> Preprocess
0:00:07.394225925 30513       0x5accf0 LOG               tinyyolov2 gsttinyyolov2.c:501:gst_tinyyolov2_postprocess:<net> Postprocess
0:00:07.394431653 30513       0x5accf0 LOG               tinyyolov2 gsttinyyolov2.c:384:print_top_predictions:<net> Box: [class:7, x:25.820670, y:11.977936, width:425.495203, height:450.224357, prob:15.204609]
0:00:07.394858915 30513       0x5accf0 LOG               tinyyolov2 gsttinyyolov2.c:479:gst_tinyyolov2_preprocess:<net> Preprocess
0:00:07.527547133 30513       0x5accf0 LOG               tinyyolov2 gsttinyyolov2.c:501:gst_tinyyolov2_postprocess:<net> Postprocess
0:00:07.527753020 30513       0x5accf0 LOG               tinyyolov2 gsttinyyolov2.c:384:print_top_predictions:<net> Box: [class:7, x:25.820670, y:11.977936, width:425.495203, height:450.224357, prob:15.204609]
0:00:07.528080219 30513       0x5accf0 LOG               tinyyolov2 gsttinyyolov2.c:479:gst_tinyyolov2_preprocess:<net> Preprocess
0:00:07.662473455 30513       0x5accf0 LOG               tinyyolov2 gsttinyyolov2.c:501:gst_tinyyolov2_postprocess:<net> Postprocess
0:00:07.662769998 30513       0x5accf0 LOG               tinyyolov2 gsttinyyolov2.c:384:print_top_predictions:<net> Box: [class:7, x:25.820670, y:11.977936, width:425.495203, height:450.224357, prob:15.204609]

TinyYolov2 inference on video file using Tensorflow

• Get the graph used on this example from this link
• You will need a video file from one of TinyYOLO classes
• Pipeline

VIDEO_FILE='cat.mp4'
MODEL_LOCATION='graph_tinyyolov2_tensorflow.pb'
INPUT_LAYER='input/Placeholder'
OUTPUT_LAYER='add_8'
GST_DEBUG=tinyyolov2:6 gst-launch-1.0 \
filesrc location=$VIDEO_FILE ! qtdemux name=demux ! h264parse ! omxh264dec ! nvvidconv ! queue ! net.sink_model \
tinyyolov2 name=net model-location=$MODEL_LOCATION backend=tensorflow backend::input-layer=$INPUT_LAYER  backend::output-layer=$OUTPUT_LAYER

• Output

0:00:07.245722660 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:479:gst_tinyyolov2_preprocess:<net> Preprocess
0:00:07.360377432 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:501:gst_tinyyolov2_postprocess:<net> Postprocess
0:00:07.360586455 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:384:print_top_predictions:<net> Box: [class:7, x:-46.105452, y:-9.139365, width:445.139551, height:487.967720, prob:14.592537]
0:00:07.360859318 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:479:gst_tinyyolov2_preprocess:<net> Preprocess
0:00:07.489190714 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:501:gst_tinyyolov2_postprocess:<net> Postprocess
0:00:07.489382873 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:384:print_top_predictions:<net> Box: [class:7, x:-46.140270, y:-9.193503, width:445.228762, height:488.028163, prob:14.596972]
0:00:07.489736216 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:479:gst_tinyyolov2_preprocess:<net> Preprocess
0:00:07.629190069 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:501:gst_tinyyolov2_postprocess:<net> Postprocess
0:00:07.629379733 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:384:print_top_predictions:<net> Box: [class:7, x:-46.281640, y:-9.164348, width:445.512899, height:487.908826, prob:14.596945]
0:00:07.629717876 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:479:gst_tinyyolov2_preprocess:<net> Preprocess
0:00:07.761072493 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:501:gst_tinyyolov2_postprocess:<net> Postprocess
0:00:07.761271244 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:384:print_top_predictions:<net> Box: [class:7, x:-46.338202, y:-9.202273, width:445.624841, height:487.954952, prob:14.592540]

TinyYolov2 inference on camera stream using Tensorflow

• Get the graph used on this example from this link
• You will need a camera compatible with Nvidia Libargus API or V4l2.

Nvidia Camera

• Pipeline

SENSOR_ID=0
MODEL_LOCATION='graph_tinyyolov2_tensorflow.pb'
INPUT_LAYER='input/Placeholder'
OUTPUT_LAYER='add_8'
GST_DEBUG=tinyyolov2:6 gst-launch-1.0 \
nvarguscamerasrc sensor-id=$SENSOR_ID ! nvvidconv ! 'video/x-raw,format=BGRx' ! queue ! net.sink_model \
tinyyolov2 name=net model-location=$MODEL_LOCATION backend=tensorflow backend::input-layer=$INPUT_LAYER  backend::output-layer=$OUTPUT_LAYER

V4L2

• Pipeline

CAMERA='/dev/video1'
MODEL_LOCATION='graph_tinyyolov2_tensorflow.pb'
INPUT_LAYER='input/Placeholder'
OUTPUT_LAYER='add_8'
GST_DEBUG=tinyyolov2:6 gst-launch-1.0 \
v4l2src device=$CAMERA ! videoconvert ! videoscale ! queue ! net.sink_model \
tinyyolov2 name=net model-location=$MODEL_LOCATION backend=tensorflow backend::input-layer=$INPUT_LAYER  backend::output-layer=$OUTPUT_LAYER

• Output

0:00:39.754924355  5030      0x10ee590 LOG               tinyyolov2 gsttinyyolov2.c:479:gst_tinyyolov2_preprocess:<net> Preprocess
0:00:39.876816786  5030      0x10ee590 LOG               tinyyolov2 gsttinyyolov2.c:501:gst_tinyyolov2_postprocess:<net> Postprocess
0:00:39.876914225  5030      0x10ee590 LOG               tinyyolov2 gsttinyyolov2.c:384:print_top_predictions:<net> Box: [class:4, x:147.260736, y:116.184709, width:134.389472, height:245.113627, prob:8.375733]
0:00:39.877085489  5030      0x10ee590 LOG               tinyyolov2 gsttinyyolov2.c:479:gst_tinyyolov2_preprocess:<net> Preprocess
0:00:39.999699614  5030      0x10ee590 LOG               tinyyolov2 gsttinyyolov2.c:501:gst_tinyyolov2_postprocess:<net> Postprocess
0:00:39.999799198  5030      0x10ee590 LOG               tinyyolov2 gsttinyyolov2.c:384:print_top_predictions:<net> Box: [class:4, x:146.957935, y:117.902112, width:134.883825, height:242.143126, prob:7.982772]
0:00:39.999962206  5030      0x10ee590 LOG               tinyyolov2 gsttinyyolov2.c:479:gst_tinyyolov2_preprocess:<net> Preprocess
0:00:40.118613969  5030      0x10ee590 LOG               tinyyolov2 gsttinyyolov2.c:501:gst_tinyyolov2_postprocess:<net> Postprocess
0:00:40.118712017  5030      0x10ee590 LOG               tinyyolov2 gsttinyyolov2.c:384:print_top_predictions:<net> Box: [class:4, x:147.147349, y:116.562615, width:134.469630, height:244.181931, prob:8.139100]
0:00:40.118882641  5030      0x10ee590 LOG               tinyyolov2 gsttinyyolov2.c:479:gst_tinyyolov2_preprocess:<net> Preprocess
0:00:40.264861052  5030      0x10ee590 LOG               tinyyolov2 gsttinyyolov2.c:501:gst_tinyyolov2_postprocess:<net> Postprocess
0:00:40.264964828  5030      0x10ee590 LOG               tinyyolov2 gsttinyyolov2.c:384:print_top_predictions:<net> Box: [class:4, x:146.618516, y:117.162739, width:135.454029, height:243.785573, prob:8.112847]

TinyYolov2 visualization with detection overlay Tensorflow

• Get the graph used on this example from this link
• You will need a camera compatible with Nvidia Libargus API or V4l2.

Nvidia Camera

• Pipeline

SENSOR_ID=0
MODEL_LOCATION='graph_tinyyolov2_tensorflow.pb'
INPUT_LAYER='input/Placeholder'
OUTPUT_LAYER='add_8'
LABELS='labels.txt'
GST_DEBUG=tinyyolov2:6 \
gst-launch-1.0 \
nvcamerasrc sensor-id=$SENSOR_ID ! 'video/x-raw(memory:NVMM)' ! tee name=t \
t. ! queue max-size-buffers=1 leaky=downstream ! nvvidconv ! 'video/x-raw,format=(string)RGBA' ! net.sink_model \
t. ! queue max-size-buffers=1 leaky=downstream ! nvvidconv ! 'video/x-raw,format=(string)RGBA' ! net.sink_bypass \
tinyyolov2 name=net backend=tensorflow model-location=$MODEL_LOCATION backend::input-layer=$INPUT_LAYER backend::output-layer=$OUTPUT_LAYER \
net.src_bypass !  detectionoverlay labels="$(cat $LABELS)" font-scale=4 thickness=4  ! nvvidconv ! nvoverlaysink sync=false -v

V4L2

• Pipeline

CAMERA='/dev/video1'
MODEL_LOCATION='graph_tinyyolov2_tensorflow.pb'
INPUT_LAYER='input/Placeholder'
OUTPUT_LAYER='add_8'
LABELS='labels.txt'
gst-launch-1.0 \
v4l2src device=$CAMERA ! "video/x-raw, width=1280, height=720" ! tee name=t \
t. ! videoconvert ! videoscale ! queue ! net.sink_model \
t. ! queue ! net.sink_bypass \
tinyyolov2 name=net model-location=$MODEL_LOCATION backend=tensorflow backend::input-layer=$INPUT_LAYER backend::output-layer=$OUTPUT_LAYER \
net.src_bypass ! videoconvert ! detectionoverlay labels="$(cat $LABELS)" font-scale=1 thickness=2 ! videoconvert ! xvimagesink sync=false

• Output

Example detection overlay output

FaceNet visualization with embedding overlay Tensorflow

• Get the graph used on this example from this link
• You will need a camera compatible with Nvidia Libargus API or V4l2.
• LABELS and EMBEDDINGS files are in $PATH_TO_GST_INFERENCE_ROOT_DIR/tests/examples/embedding/embeddings.

Nvidia Camera

• Pipeline

SENSOR_ID=0
MODEL_LOCATION='graph_facenetv1_tensorflow.pb'
INPUT_LAYER='input'
OUTPUT_LAYER='output'
LABELS='$PATH_TO_GST_INFERENCE_ROOT_DIR/tests/examples/embedding/embeddings/labels.txt'
EMBEDDINGS='$PATH_TO_GST_INFERENCE_ROOT_DIR/tests/examples/embedding/embeddings/embeddings.txt'
gst-launch-1.0 \
nvcamerasrc sensor-id=$SENSOR_ID ! 'video/x-raw(memory:NVMM),width=(int)1280,height=(int)720' ! nvvidconv ! 'video/x-raw,format=BGRx,width=(int)1280,height=(int)720' ! videoconvert ! tee name=t \
t. ! queue ! videoscale ! queue ! net.sink_model \
t. ! queue ! net.sink_bypass \
facenetv1 name=net model-location=$MODEL_LOCATION backend=tensorflow backend::input-layer=$INPUT_LAYER backend::output-layer=$OUTPUT_LAYER \
net.src_bypass ! videoconvert ! embeddingoverlay labels="$(cat $LABELS)" embeddings="$(cat $EMBEDDINGS)" font-scale=4 thickness=4 ! videoconvert ! xvimagesink sync=false

V4L2

• Pipeline

CAMERA='/dev/video1'
MODEL_LOCATION='graph_facenetv1_tensorflow.pb'
INPUT_LAYER='input'
OUTPUT_LAYER='output'
LABELS='$PATH_TO_GST_INFERENCE_ROOT_DIR/tests/examples/embedding/embeddings/labels.txt'
EMBEDDINGS='$PATH_TO_GST_INFERENCE_ROOT_DIR/tests/examples/embedding/embeddings/embeddings.txt'
gst-launch-1.0 \
v4l2src device=$CAMERA ! "video/x-raw, width=1280, height=720" ! tee name=t \
t. ! videoconvert ! videoscale ! queue ! net.sink_model \
t. ! queue ! net.sink_bypass \
facenetv1 name=net model-location=$MODEL_LOCATION backend=tensorflow backend::input-layer=$INPUT_LAYER backend::output-layer=$OUTPUT_LAYER \
net.src_bypass ! videoconvert ! embeddingoverlay labels="$(cat $LABELS)" embeddings="$(cat $EMBEDDINGS)" font-scale=4 thickness=4 ! videoconvert ! xvimagesink sync=false

• Output

Example embedding overlay output

TensorFlow-Lite

Inceptionv4 inference on image file using TensorFlow-Lite

• Get the graph used on this example from this link
• You will need an image file from one of ImageNet classes
• Pipeline

IMAGE_FILE='cat.jpg'
MODEL_LOCATION='graph_inceptionv4.tflite'
LABELS='labels.txt'
GST_DEBUG=inceptionv4:6 gst-launch-1.0 \
multifilesrc location=$IMAGE_FILE ! jpegparse ! nvjpegdec ! 'video/x-raw' ! nvvidconv ! 'video/x-raw(memory:NVMM),format=NV12' ! nvvidconv ! queue ! net.sink_model \
inceptionv4 name=net model-location=$MODEL_LOCATION backend=tflite labels="$(cat $LABELS)"

• Output

0:02:22.005527960 30355       0x5accf0 LOG              inceptionv4 gstinceptionv4.c:200:gst_inceptionv4_preprocess:<net> Preprocess
0:02:22.168796723 30355       0x5accf0 LOG              inceptionv4 gstinceptionv4.c:232:gst_inceptionv4_postprocess:<net> Postprocess
0:02:22.168947603 30355       0x5accf0 LOG              inceptionv4 gstinceptionv4.c:253:gst_inceptionv4_postprocess:<net> Highest probability is label 282 : (0.627314)
0:02:22.169237202 30355       0x5accf0 LOG              inceptionv4 gstinceptionv4.c:200:gst_inceptionv4_preprocess:<net> Preprocess
0:02:22.339393463 30355       0x5accf0 LOG              inceptionv4 gstinceptionv4.c:232:gst_inceptionv4_postprocess:<net> Postprocess
0:02:22.339496918 30355       0x5accf0 LOG              inceptionv4 gstinceptionv4.c:253:gst_inceptionv4_postprocess:<net> Highest probability is label 282 : (0.627314)
0:02:22.339701878 30355       0x5accf0 LOG              inceptionv4 gstinceptionv4.c:200:gst_inceptionv4_preprocess:<net> Preprocess
0:02:22.507804674 30355       0x5accf0 LOG              inceptionv4 gstinceptionv4.c:232:gst_inceptionv4_postprocess:<net> Postprocess
0:02:22.507950081 30355       0x5accf0 LOG              inceptionv4 gstinceptionv4.c:253:gst_inceptionv4_postprocess:<net> Highest probability is label 282 : (0.627314)
0:02:22.508232128 30355       0x5accf0 LOG              inceptionv4 gstinceptionv4.c:200:gst_inceptionv4_preprocess:<net> Preprocess
0:02:22.678740356 30355       0x5accf0 LOG              inceptionv4 gstinceptionv4.c:232:gst_inceptionv4_postprocess:<net> Postprocess
0:02:22.678892356 30355       0x5accf0 LOG              inceptionv4 gstinceptionv4.c:253:gst_inceptionv4_postprocess:<net> Highest probability is label 282 : (0.627314)

Inceptionv4 inference on video file using TensorFlow-Lite

• Get the graph used on this example from this link
• You will need a video file from one of ImageNet classes
• Pipeline

VIDEO_FILE='cat.mp4'
MODEL_LOCATION='graph_inceptionv4.tflite'
LABELS='labels.txt'
GST_DEBUG=inceptionv4:6 gst-launch-1.0 \
filesrc location=$VIDEO_FILE ! qtdemux name=demux ! h264parse ! omxh264dec ! nvvidconv ! queue ! net.sink_model \
inceptionv4 name=net model-location=$MODEL_LOCATION backend=tflite labels="$(cat $LABELS)"

• Output

0:00:11.728307018 30399       0x5ad000 LOG              inceptionv4 gstinceptionv4.c:200:gst_inceptionv4_preprocess:<net> Preprocess
0:00:11.892030154 30399       0x5ad000 LOG              inceptionv4 gstinceptionv4.c:232:gst_inceptionv4_postprocess:<net> Postprocess
0:00:11.892258185 30399       0x5ad000 LOG              inceptionv4 gstinceptionv4.c:253:gst_inceptionv4_postprocess:<net> Highest probability is label 282 : (0.686857)
0:00:11.892556808 30399       0x5ad000 LOG              inceptionv4 gstinceptionv4.c:200:gst_inceptionv4_preprocess:<net> Preprocess
0:00:12.065318539 30399       0x5ad000 LOG              inceptionv4 gstinceptionv4.c:232:gst_inceptionv4_postprocess:<net> Postprocess
0:00:12.065467786 30399       0x5ad000 LOG              inceptionv4 gstinceptionv4.c:253:gst_inceptionv4_postprocess:<net> Highest probability is label 282 : (0.673300)
0:00:12.065759849 30399       0x5ad000 LOG              inceptionv4 gstinceptionv4.c:200:gst_inceptionv4_preprocess:<net> Preprocess
0:00:12.247159695 30399       0x5ad000 LOG              inceptionv4 gstinceptionv4.c:232:gst_inceptionv4_postprocess:<net> Postprocess
0:00:12.247309295 30399       0x5ad000 LOG              inceptionv4 gstinceptionv4.c:253:gst_inceptionv4_postprocess:<net> Highest probability is label 282 : (0.669102)
0:00:12.247612718 30399       0x5ad000 LOG              inceptionv4 gstinceptionv4.c:200:gst_inceptionv4_preprocess:<net> Preprocess
0:00:12.419172436 30399       0x5ad000 LOG              inceptionv4 gstinceptionv4.c:232:gst_inceptionv4_postprocess:<net> Postprocess
0:00:12.419321396 30399       0x5ad000 LOG              inceptionv4 gstinceptionv4.c:253:gst_inceptionv4_postprocess:<net> Highest probability is label 282 : (0.667991)

Inceptionv4 inference on camera stream using TensorFlow-Lite

• Get the graph used on this example from this link
• You will need a camera compatible with Nvidia Libargus API or V4l2.

Nvidia Camera

• Pipeline

SENSOR_ID=0
MODEL_LOCATION='graph_inceptionv4.tflite'
LABELS='labels.txt'
GST_DEBUG=inceptionv4:6 gst-launch-1.0 \
nvcamerasrc sensor-id=$SENSOR_ID ! nvvidconv ! queue ! net.sink_model \
inceptionv4 name=net backend=tflite model-location=$MODEL_LOCATION labels="$(cat $LABELS)"

V4L2

• Pipeline

CAMERA='/dev/video0'
MODEL_LOCATION='graph_inceptionv4.tflite'
LABELS='labels.txt'
GST_DEBUG=inceptionv4:6 gst-launch-1.0 \
v4l2src device=$CAMERA ! videoconvert ! videoscale ! queue ! net.sink_model \
inceptionv4 name=net model-location=$MODEL_LOCATION backend=tflite labels="$(cat $LABELS)"

• Output

0:00:12.199657219  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:200:gst_inceptionv4_preprocess:<net> Preprocess
0:00:12.365172092  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:232:gst_inceptionv4_postprocess:<net> Postprocess
0:00:12.365271548  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:253:gst_inceptionv4_postprocess:<net> Highest probability is label 774 : (0.196048)
0:00:12.365421435  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:200:gst_inceptionv4_preprocess:<net> Preprocess
0:00:12.530604726  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:232:gst_inceptionv4_postprocess:<net> Postprocess
0:00:12.530700501  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:253:gst_inceptionv4_postprocess:<net> Highest probability is label 774 : (0.179406)
0:00:12.530848565  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:200:gst_inceptionv4_preprocess:<net> Preprocess
0:00:12.697053611  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:232:gst_inceptionv4_postprocess:<net> Postprocess
0:00:12.697147818  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:253:gst_inceptionv4_postprocess:<net> Highest probability is label 774 : (0.144033)
0:00:12.697295530  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:200:gst_inceptionv4_preprocess:<net> Preprocess
0:00:12.862007878  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:232:gst_inceptionv4_postprocess:<net> Postprocess
0:00:12.862104134  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:253:gst_inceptionv4_postprocess:<net> Highest probability is label 774 : (0.157707)
0:00:12.862252645  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:200:gst_inceptionv4_preprocess:<net> Preprocess
0:00:13.027090881  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:232:gst_inceptionv4_postprocess:<net> Postprocess
0:00:13.027190273  4675      0x10ee590 LOG              inceptionv4 gstinceptionv4.c:253:gst_inceptionv4_postprocess:<net> Highest probability is label 774 : (0.142998)

Inceptionv4 visualization with classification overlay TensorFlow-Lite

• Get the graph used on this example from this link
• You will need a camera compatible with Nvidia Libargus API or V4l2.

Nvidia Camera

• Pipeline

SENSOR_ID=0
MODEL_LOCATION='graph_inceptionv4.tflite'
LABELS='labels.txt'
gst-launch-1.0 \
nvcamerasrc sensor-id=$SENSOR_ID ! 'video/x-raw(memory:NVMM)' ! tee name=t \
t. ! queue max-size-buffers=1 leaky=downstream ! nvvidconv ! 'video/x-raw,format=(string)RGBA' ! net.sink_model \
t. ! queue max-size-buffers=1 leaky=downstream ! nvvidconv ! 'video/x-raw,format=(string)RGBA' ! net.sink_bypass \
inceptionv4 name=net backend=tflite model-location=$MODEL_LOCATION labels="(cat $LABELS)" \
net.src_bypass ! classificationoverlay labels="$(cat $LABELS)" font-scale=4 thickness=4 ! nvvidconv ! nvoverlaysink sync=false -v

V4L2

• Pipeline

CAMERA='/dev/video1'
MODEL_LOCATION='graph_inceptionv4.tflite'
LABELS='labels.txt'
gst-launch-1.0 \
v4l2src device=$CAMERA ! "video/x-raw, width=1280, height=720" ! tee name=t \
t. ! videoconvert ! videoscale ! queue ! net.sink_model \
t. ! queue ! net.sink_bypass \
inceptionv4 name=net backend=tflite model-location=$MODEL_LOCATION labels="(cat $LABELS)" \
net.src_bypass ! videoconvert ! classificationoverlay labels="$(cat $LABELS)" font-scale=4 thickness=4 ! videoconvert ! xvimagesink sync=false

• Output

Example classification overlay output

TinyYolov2 inference on image file using TensorFlow-Lite

• Get the graph used on this example from this link
• You will need an image file from one of TinyYOLO classes
• Pipeline

IMAGE_FILE='cat.jpg'
MODEL_LOCATION='graph_tinyyolov2.tflite'
LABELS='labels.txt'
GST_DEBUG=tinyyolov2:6 gst-launch-1.0 \
multifilesrc location=$IMAGE_FILE ! jpegparse ! nvjpegdec ! 'video/x-raw' ! nvvidconv ! 'video/x-raw(memory:NVMM),format=NV12' ! nvvidconv ! queue ! net.sink_model \
tinyyolov2 name=net backend=tflite model-location=$MODEL_LOCATION labels="(cat $LABELS)"

• Output

0:00:07.137677204 30513       0x5accf0 LOG               tinyyolov2 gsttinyyolov2.c:479:gst_tinyyolov2_preprocess:<net> Preprocess
0:00:07.266928985 30513       0x5accf0 LOG               tinyyolov2 gsttinyyolov2.c:501:gst_tinyyolov2_postprocess:<net> Postprocess
0:00:07.267080761 30513       0x5accf0 LOG               tinyyolov2 gsttinyyolov2.c:384:print_top_predictions:<net> Box: [class:7, x:25.820670, y:11.977936, width:425.495203, height:450.224357, prob:15.204609]
0:00:07.267382968 30513       0x5accf0 LOG               tinyyolov2 gsttinyyolov2.c:479:gst_tinyyolov2_preprocess:<net> Preprocess
0:00:07.394225925 30513       0x5accf0 LOG               tinyyolov2 gsttinyyolov2.c:501:gst_tinyyolov2_postprocess:<net> Postprocess
0:00:07.394431653 30513       0x5accf0 LOG               tinyyolov2 gsttinyyolov2.c:384:print_top_predictions:<net> Box: [class:7, x:25.820670, y:11.977936, width:425.495203, height:450.224357, prob:15.204609]
0:00:07.394858915 30513       0x5accf0 LOG               tinyyolov2 gsttinyyolov2.c:479:gst_tinyyolov2_preprocess:<net> Preprocess
0:00:07.527547133 30513       0x5accf0 LOG               tinyyolov2 gsttinyyolov2.c:501:gst_tinyyolov2_postprocess:<net> Postprocess
0:00:07.527753020 30513       0x5accf0 LOG               tinyyolov2 gsttinyyolov2.c:384:print_top_predictions:<net> Box: [class:7, x:25.820670, y:11.977936, width:425.495203, height:450.224357, prob:15.204609]
0:00:07.528080219 30513       0x5accf0 LOG               tinyyolov2 gsttinyyolov2.c:479:gst_tinyyolov2_preprocess:<net> Preprocess
0:00:07.662473455 30513       0x5accf0 LOG               tinyyolov2 gsttinyyolov2.c:501:gst_tinyyolov2_postprocess:<net> Postprocess
0:00:07.662769998 30513       0x5accf0 LOG               tinyyolov2 gsttinyyolov2.c:384:print_top_predictions:<net> Box: [class:7, x:25.820670, y:11.977936, width:425.495203, height:450.224357, prob:15.204609]

TinyYolov2 inference on video file using TensorFlow-Lite

• Get the graph used on this example from this link
• You will need a video file from one of TinyYOLO classes
• Pipeline

VIDEO_FILE='cat.mp4'
MODEL_LOCATION='graph_tinyyolov2.tflite'
LABELS='labels.txt'
GST_DEBUG=tinyyolov2:6 gst-launch-1.0 \
filesrc location=$VIDEO_FILE ! qtdemux name=demux ! h264parse ! omxh264dec ! nvvidconv ! queue ! net.sink_model \
tinyyolov2 name=net backend=tflite model-location=$MODEL_LOCATION labels="(cat $LABELS)"

• Output

0:00:07.245722660 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:479:gst_tinyyolov2_preprocess:<net> Preprocess
0:00:07.360377432 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:501:gst_tinyyolov2_postprocess:<net> Postprocess
0:00:07.360586455 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:384:print_top_predictions:<net> Box: [class:7, x:-46.105452, y:-9.139365, width:445.139551, height:487.967720, prob:14.592537]
0:00:07.360859318 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:479:gst_tinyyolov2_preprocess:<net> Preprocess
0:00:07.489190714 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:501:gst_tinyyolov2_postprocess:<net> Postprocess
0:00:07.489382873 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:384:print_top_predictions:<net> Box: [class:7, x:-46.140270, y:-9.193503, width:445.228762, height:488.028163, prob:14.596972]
0:00:07.489736216 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:479:gst_tinyyolov2_preprocess:<net> Preprocess
0:00:07.629190069 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:501:gst_tinyyolov2_postprocess:<net> Postprocess
0:00:07.629379733 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:384:print_top_predictions:<net> Box: [class:7, x:-46.281640, y:-9.164348, width:445.512899, height:487.908826, prob:14.596945]
0:00:07.629717876 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:479:gst_tinyyolov2_preprocess:<net> Preprocess
0:00:07.761072493 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:501:gst_tinyyolov2_postprocess:<net> Postprocess
0:00:07.761271244 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:384:print_top_predictions:<net> Box: [class:7, x:-46.338202, y:-9.202273, width:445.624841, height:487.954952, prob:14.592540]

TinyYolov2 inference on camera stream using TensorFlow-Lite

• Get the graph used on this example from this link
• You will need a camera compatible with Nvidia Libargus API or V4l2.

Nvidia Camera

• Pipeline

SENSOR_ID=0
MODEL_LOCATION='graph_tinyyolov2.tflite'
LABELS='labels.txt'
GST_DEBUG=tinyyolov2:6 gst-launch-1.0 \
nvarguscamerasrc sensor-id=$SENSOR_ID ! nvvidconv ! 'video/x-raw,format=BGRx' ! queue ! net.sink_model \
tinyyolov2 name=net backend=tflite model-location=$MODEL_LOCATION labels="(cat $LABELS)"

V4L2

• Pipeline

CAMERA='/dev/video1'
MODEL_LOCATION='graph_tinyyolov2.tflite'
LABELS='labels.txt'
GST_DEBUG=tinyyolov2:6 gst-launch-1.0 \
v4l2src device=$CAMERA ! videoconvert ! videoscale ! queue ! net.sink_model \
tinyyolov2 name=net model-location=$MODEL_LOCATION backend=tflite labels="$(cat $LABELS)"

• Output

0:00:39.754924355  5030      0x10ee590 LOG               tinyyolov2 gsttinyyolov2.c:479:gst_tinyyolov2_preprocess:<net> Preprocess
0:00:39.876816786  5030      0x10ee590 LOG               tinyyolov2 gsttinyyolov2.c:501:gst_tinyyolov2_postprocess:<net> Postprocess
0:00:39.876914225  5030      0x10ee590 LOG               tinyyolov2 gsttinyyolov2.c:384:print_top_predictions:<net> Box: [class:4, x:147.260736, y:116.184709, width:134.389472, height:245.113627, prob:8.375733]
0:00:39.877085489  5030      0x10ee590 LOG               tinyyolov2 gsttinyyolov2.c:479:gst_tinyyolov2_preprocess:<net> Preprocess
0:00:39.999699614  5030      0x10ee590 LOG               tinyyolov2 gsttinyyolov2.c:501:gst_tinyyolov2_postprocess:<net> Postprocess
0:00:39.999799198  5030      0x10ee590 LOG               tinyyolov2 gsttinyyolov2.c:384:print_top_predictions:<net> Box: [class:4, x:146.957935, y:117.902112, width:134.883825, height:242.143126, prob:7.982772]
0:00:39.999962206  5030      0x10ee590 LOG               tinyyolov2 gsttinyyolov2.c:479:gst_tinyyolov2_preprocess:<net> Preprocess
0:00:40.118613969  5030      0x10ee590 LOG               tinyyolov2 gsttinyyolov2.c:501:gst_tinyyolov2_postprocess:<net> Postprocess
0:00:40.118712017  5030      0x10ee590 LOG               tinyyolov2 gsttinyyolov2.c:384:print_top_predictions:<net> Box: [class:4, x:147.147349, y:116.562615, width:134.469630, height:244.181931, prob:8.139100]
0:00:40.118882641  5030      0x10ee590 LOG               tinyyolov2 gsttinyyolov2.c:479:gst_tinyyolov2_preprocess:<net> Preprocess
0:00:40.264861052  5030      0x10ee590 LOG               tinyyolov2 gsttinyyolov2.c:501:gst_tinyyolov2_postprocess:<net> Postprocess
0:00:40.264964828  5030      0x10ee590 LOG               tinyyolov2 gsttinyyolov2.c:384:print_top_predictions:<net> Box: [class:4, x:146.618516, y:117.162739, width:135.454029, height:243.785573, prob:8.112847]

TinyYolov2 visualization with detection overlay TensorFlow-Lite

• Get the graph used on this example from this link
• You will need a camera compatible with Nvidia Libargus API or V4l2.

Nvidia Camera

• Pipeline

SENSOR_ID=0
MODEL_LOCATION='graph_tinyyolov2.tflite'
LABELS='labels.txt'
GST_DEBUG=tinyyolov2:6 \
gst-launch-1.0 \
nvcamerasrc sensor-id=$SENSOR_ID ! 'video/x-raw(memory:NVMM)' ! tee name=t \
t. ! queue max-size-buffers=1 leaky=downstream ! nvvidconv ! 'video/x-raw,format=(string)RGBA' ! net.sink_model \
t. ! queue max-size-buffers=1 leaky=downstream ! nvvidconv ! 'video/x-raw,format=(string)RGBA' ! net.sink_bypass \
tinyyolov2 name=net backend=tflite model-location=$MODEL_LOCATION labels="(cat $LABELS)" \
net.src_bypass !  detectionoverlay labels="$(cat $LABELS)" font-scale=4 thickness=4  ! nvvidconv ! nvoverlaysink sync=false -v

V4L2

• Pipeline

CAMERA='/dev/video1'
MODEL_LOCATION='graph_tinyyolov2.tflite'
LABELS='labels.txt'
gst-launch-1.0 \
v4l2src device=$CAMERA ! "video/x-raw, width=1280, height=720" ! tee name=t \
t. ! videoconvert ! videoscale ! queue ! net.sink_model \
t. ! queue ! net.sink_bypass \
tinyyolov2 name=net backend=tflite model-location=$MODEL_LOCATION labels="(cat $LABELS)" \
net.src_bypass ! videoconvert ! detectionoverlay labels="$(cat $LABELS)" font-scale=1 thickness=2 ! videoconvert ! xvimagesink sync=false

• Output

Example detection overlay output