cd into the ML directory before copy and pasting these commands cd ML

First download your images from google using google_images_download python3 utils/google_images_download.py --keywords "republic services truck" --limit 100 --output_directory data/images --chromedriver loc/to/chromedriver

After you download the images let's move the images from the download folder to data/images for now. Let's convert all of our images to jpg before we continue. python3 utils/imgconvert.py --folder data/images

We'll need to annotate our images. set your bounding boxes for your images. We'll use LabelImg After compiling LabelImg from the link above, you can open it with: python3 path/to/labelImg.py data/images

After we're done labeling our images you'll have a bunch of xml files. We need to convert the XML files that labelimg created to csv. But before we do that let's split up our images into two different folders: train and test. mkdir data/train && mkdir data/test

Also create a open-images-model-labels.txt file inside data/ as well and input all of your class names. 1 per line: touch open-images-model-labels.txt

BACKGROUND
garbage_truck

As well as a labels.txt file: labels.txt

garbage_truck

A good rule of thumb is to put 80% of the images inside train and 20% inside test. Make sure when splitting up the images you copy the xml files as well.

Now we can run xml2csv: python3 utils/xml2csv.py --folder data/ --dest data/

We're all set to start training now Let's first download the pretrained mobilenet model from Nvidia since they so kindly already did most of the work for us: wget https://nvidia.box.com/shared/static/djf5w54rjvpqocsiztzaandq1m3avr7c.pth -O train/models/mobilenet-v1-ssd-mp-0_675.pth

We'll be using the Pytorch-ssd library to make things really simple for us. python3 ssd-train/train_ssd.py --dataset_type open_images --datasets data --net mb1-ssd --pretrained_ssd ssd-train/models/mobilenet-v1-ssd-mp-0_675.pth --num_epochs 30 --batch_size 4 --num_workers=0

Now we should have our model. We can now test it out on an image: python3 ssd-train/run_ssd_example.py mb1-ssd data/mb1-ssd-Epoch-29-Loss-1.8117772539456685.pth data/open-images-model-labels.txt data/test/86.refuse-garbage.jpg

You'll now see your output image at data/output.jpg with the image labeled.

From here we're going to be converting things so we can use it on our Nvidia Jetson devices.

First we'll convert our model from pytorch to onnx so we can load it with TensorRT on our Jetson python3 ssd-train/onnx_export.py --model-dir=data --labels=open-images-model-labels.txt

We will send these files over to the Jetson:

data/mb1-ssd-Epoch-29-Loss-1.8117772539456685.pth
data/open-images-model-labels.txt
detectnet.py

Build Jetson-Inference on your Jetson device before continuing

Tip: If you want to run commands from ssh but display it on the screen of the jetson, run: export DISPLAY=:0 before running the script

We're now ready to test some images with our new TensorRT model.

cd into the Run directory cd ../Run

If you're running this with a Pi, copy the whole Run/pi directory to your Pi and start the flask server on your pi

Now on the jetson you can run: python3 watch_cam_rt.py --net_type=ssd-mobilenet-v2 --model=data/ssd-mobilenet.onnx --labels=data/open-images-model-labels.txt --input=/dev/video0 --show

Or without TensorRT: python3 watch_cam.py --net_type=mb1-ssd --model_path=data/mb1-ssd-Epoch-29-Loss-1.8117772539456685.pth --label_path=data/open-images-model-labels.txt --input=0 --show