detectnet-console.md

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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 optional --network flag which changes the detection model being used (the default network is PedNet).

Here's an example of locating humans in an image with the default PedNet model:

C++

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

Python

$ ./detectnet-console.py peds-004.jpg output.jpg

Pre-trained Detection Models Available

Below is a table of the pre-trained object detection networks available for download, and the associated --network argument to detectnet-console used for loading the pre-trained models:

Model	CLI argument	NetworkType enum	Object classes
SSD-Mobilenet-v1	ssd-mobilenet-v1	SSD_MOBILENET_V1	91 (COCO classes)
SSD-Mobilenet-v2	ssd-mobilenet-v2	SSD_MOBILENET_V2	91 (COCO classes)
SSD-Inception-v2	ssd-inception-v2	SSD_INCEPTION_V2	91 (COCO classes)
DetectNet-COCO-Dog	coco-dog	COCO_DOG	dogs
DetectNet-COCO-Bottle	coco-bottle	COCO_BOTTLE	bottles
DetectNet-COCO-Chair	coco-chair	COCO_CHAIR	chairs
DetectNet-COCO-Airplane	coco-airplane	COCO_AIRPLANE	airplanes
ped-100	pednet	PEDNET	pedestrians
multiped-500	multiped	PEDNET_MULTI	pedestrians, luggage
facenet-120	facenet	FACENET	faces

note: to download additional networks, run the Model Downloader tool
             $ cd jetson-inference/tools
             $ ./download-models.sh

Running Different Detection Models

You can specify which model to load by setting the --network flag on the command line to one of the corresponding CLI arguments from the table above. By default, PedNet is loaded (pedestrian detection) if the optional --network flag isn't specified.

Let's try running some of the other COCO models:

# C++
$ ./detectnet-console --network=coco-dog dog_1.jpg output_1.jpg

# Python
$ ./detectnet-console.py --network=coco-dog dog_1.jpg output_1.jpg

# C++
$ ./detectnet-console --network=coco-bottle bottle_0.jpg output_2.jpg

# Python
$ ./detectnet-console.py --network=coco-bottle bottle_0.jpg output_2.jpg

# C++
$ ./detectnet-console --network=coco-airplane airplane_0.jpg output_3.jpg 

# Python
$ ./detectnet-console.py --network=coco-airplane airplane_0.jpg output_3.jpg

Multi-class Object Detection Models

Some models support the detection of multiple types of objects. For example, when using the multiped model on images containing luggage or baggage in addition to pedestrians, the 2nd object class is rendered with a green overlay:

# C++
$ ./detectnet-console --network=multiped peds-003.jpg output_4.jpg

# Python
$ ./detectnet-console.py --network=multiped peds-003.jpg output_4.jpg

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

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