detectnet-console.md

^{Back | Next | Contents
Object Detection}

Detecting Objects from the Command Line

To process test images with detectNet and TensorRT on the Jetson, we can use the detectnet-console program.

detectnet-console accepts command-line arguments representing the path to the input image and path to the output image (with the bounding box overlays rendered). Some test images are also included with the repo.

To specify your model that you downloaded from DIGITS in the previous step, use the syntax to detectnet-console below. First, for convienience, set the path to your extracted snapshot into a $NET variable:

$ NET=20170504-190602-879f_epoch_100

$ ./detectnet-console dog_0.jpg output_0.jpg \
--prototxt=$NET/deploy.prototxt \
--model=$NET/snapshot_iter_38600.caffemodel \
--input_blob=data \ 
--output_cvg=coverage \
--output_bbox=bboxes

note: the input_blob, output_cvg, and output_bbox arguments may be omitted if your DetectNet layer names match the defaults above (i.e. if you are using the prototxt from following this tutorial). These optional command line parameters are provided if you are using a customized DetectNet with different layer names.

Launching With a Pretrained Model

Alternatively, to load one of the pretrained snapshots that comes with the repo, you can specify the pretrained model name as the 3rd argument to detectnet-console:

$ ./detectnet-console dog_1.jpg output_1.jpg coco-dog

The above command will process dog_1.jpg, saving it to output_1.jpg, using the pretrained DetectNet-COCO-Dog model. This is a shortcut of sorts so you don't need to wait for the model to complete training if you don't want to.

Pretrained DetectNet Models Available

Below is a table of the pretrained DetectNet snapshots downloaded with the repo (located in the data/networks directory after running cmake step) and the associated argument to detectnet-console used for loading the pretrained model:

DIGITS model	CLI argument	classes
DetectNet-COCO-Airplane	coco-airplane	airplanes
DetectNet-COCO-Bottle	coco-bottle	bottles
DetectNet-COCO-Chair	coco-chair	chairs
DetectNet-COCO-Dog	coco-dog	dogs
ped-100	pednet	pedestrians
multiped-500	multiped	pedestrians, luggage
facenet-120	facenet	faces

These all also have the python layer patch above already applied.

Running Other MS-COCO Models on Jetson

Let's try running some of the other COCO models. The training data for these are all included in the dataset downloaded above. Although the DIGITS training example above was for the coco-dog model, the same procedure can be followed to train DetectNet on the other classes included in the sample COCO dataset.

$ ./detectnet-console bottle_0.jpg output_2.jpg coco-bottle

$ ./detectnet-console airplane_0.jpg output_3.jpg coco-airplane

Running Pedestrian Models on Jetson

Included in the repo are also DetectNet models pretrained to detect humans. The pednet and multiped models recognized pedestrians while facenet recognizes faces (from FDDB). Here's an example of detecting multiple humans simultaneously in a crowded space:

$ ./detectnet-console peds-004.jpg output-4.jpg multiped

Multi-class Object Detection Models

When using the multiped model (PEDNET_MULTI), for images containing luggage or baggage in addition to pedestrians, the 2nd object class is rendered with a green overlay.

$ ./detectnet-console peds-003.jpg output-3.jpg multiped

Next, we'll run object detection on a live camera stream.

Next | Running the Live Camera Detection Demo
Back | Downloading the Detection Model to Jetson

^{© 2016-2019 NVIDIA | Table of Contents}