This text assumes all the relevant software is already installed on the Jetson.
Prerequisites:
Jetson TX2 flashed with JetPack 3.3.
Caffe version: 0.15.14
DIGITS version: 6.1.1
Check that all software is installed correctly by using the pre-installed dog detect model that comes with Jetpack by running this in terminal:
$ sudo ~/jetson_clocks.sh && cd jetson-inference/build/aarch64/bin && ./detectnet-camera coco-dog
It will take a few minutes to load up before the camera footage appears.
Turn on the DIGITS server:
$ sudo ~/jetson_clocks.sh && cd digits && export CAFFE_ROOT=/home/nvidia/caffe && ./digits-devserver
Now we’re going to build the model using actual images of dogs with their associated text files:
In browser naviate to http://localhost:5000/
Importing the Detection Dataset into DIGITS:
Datasets > Images > Object Detection
Training image folder: /media/nvidia/2037-F6FA/coco/train/images/dog
Training label folder: /media/nvidia/2037-F6FA/coco/train/labels/dog
Validation image folder: /media/nvidia/2037-F6FA/coco/val/images/dog
Validation label folder: /media/nvidia/2037-F6FA/coco/val/labels/dog
Pad image (Width x Height): 640 x 640
Custom classes: dontcare, dog
Group Name: MS-COCO
Dataset Name: coco-dog
Create
Home > Models > Images > Object Detection
Select Dataset: coco-dog
Training epochs = 16
Snapshot interval (in epochs) = 16
Validation interval (in epochs) = 16
Subtract Mean: none
Solver Type: Adam
Base learning rate: 2.5e-05
Show advanced learning options
Policy: Exponential Decay
Gamma: 0.99
batch size = 2
batch accumulation = 5 (for training on Jetson TX2)
Specifying the DetectNet Prototxt:
Custom Network > Caffe
The DetectNet prototxt is located at /home/nvidia/jetson-inference/data/networks/detectnet.prototxt in the repo.
Pretrained Model = /home/nvidia/jetson-inference/data/networks/bvlc_googlenet.caffemodel
Create
Location of epoch snapshots: /home/nvidia/digits/digits/jobs
You should see the model being created through a series of epochs. Make a note of the final epoch.
Navigate to /home/nvidia/digits/digits/jobs and open the latest job folder and check it has the ‘snapshot_iter_.caffemodel’ files in it. Make a note of the highest '’ number then copy and paste the folder into here for deployment: /home/nvidia/jetson-inference/build/aarch64/bin.
Rename the folder to reflect the number of epochs that it passed, eg myDogModel_epoch_30.
For Jetson TX2, at the end of deploy.prototxt, delete the layer named cluster:
layer {
name: “cluster”
type: “Python”
bottom: “coverage”
bottom: “bboxes”
top: “bbox-list”
python_param {
module: “caffe.layers.detectnet.clustering”
layer: “ClusterDetections”
param_str: “640, 640, 16, 0.6, 2, 0.02, 22, 1”
}
}
Open terminal and run, changing the ‘*****’ number accordingly:
$ cd jetson-inference/build/aarch64/bin && NET=myDogModel_epoch_30 && ./detectnet-camera
–prototxt=$NET/deploy.prototxt
–model=$NET/snapshot_iter_*****.caffemodel
Hit return twice and you’ll see various messges including:
[TRT] attempting to open cache file dogPoo_epoch_8/snapshot_iter_3088.caffemodel.2.tensorcache
[TRT] cache file not found, profiling network model
This is not an error!
If you’ve got:
[TRT] building CUDA engine
Then all is good - just wait a few minutes for it to complete and then the camera should activate.
Now find / borrow a dog and test for bounding boxes!