To detect Asian hornets a large set of photos need to be taken and boundary boxes 'drawn' around the actual insect to help determine where it is in the camera frame. Since we dont actually have any hornets here yet, I've used a ready prepared set of dog photos as a practice run. 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!