The Many Features of Apache MXNet GluonCV
Let's take a quick look at how to use GluonCV for open source computer vision with Deep Learning.
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In preparation for my talk at the Philadelphia Open Source Conference, Apache Deep Learning 201, I wanted to have some good images for running various Apache MXNet Deep Learning Algorithms for Computer Vision.
Using Apache open source tools - Apache NiFi 1.8 and Apache MXNet 1.3 with GluonCV I can easily ingest live traffic camera images and run Object Detection, Semantic Segmentation and Instance Segmentation.
It's so easy, I am running multiple on the data to see which gives me the results I like. I am like YOLO, which is one of my old favorites.
So, we can find the cars and people in these webcams. Use cases can be around traffic optimization, public safety, and advertisement optimization. Due to licensing, I thought it better not to show Traffic Camera data here.
To industrialize and scale out this process from a single Data Scientist to a national ingestion system, we use the power of Apache NiFi to ingest, process and control flows. I am using the latest Apache NiFi 1.8.
Apache NiFi Flow to Ingest and Process Traffic Camera Data
First, we have a list of URLs that I want to process, and this can be sourced and stored anywhere. For ease of use with a static set, I am using GenerateFlowFile. I have a JSON file of URLs that I split and parse to call various Computer Vision Python scripts (DeepLab3, MaskRCNN, YOLO and others). YOLO seems to be the most useful so far. I am grabbing the results, some system metrics, metadata, and the Deep Learning analytics generated by Apache MXNet.
I split the flow into two portions. One builds GluonCV result data from YOLO and the other creates a file from TensorFlow results done on the fly.
Here is a list of my webcam URLs. There are millions of them out there.
If your data is tabular, then you need a schema for fast record processing.
An Example Dataset Returned from GLUONCV — YOLO Python 3.6 Script
I turn JSON data into HDFS Writeable AVRO Data and can run live SQL on it
One Output Source Code Be a Joint Slack Group
Object Detection: GluonCV YOLO v3 and Apache NiFi
This can be OpenCV, a static photo or from a URL.
Object Detection: Faster RCNN with GluonCV
Faster RCNN model trained on Pascal VOC dataset with ResNet-50 backbone
net = gcv.model_zoo.get_model(faster_rcnn_resnet50_v1b_voc, pretrained=True)
Instance Segmentation: Mask RCNN with GluonCV
Mask RCNN model trained on COCO dataset with ResNet-50 backbone
net = model_zoo.get_model('mask_rcnn_resnet50_v1b_coco', pretrained=True)
There are a lot of people crossing the street!
Semantic Segmentation: DeepLabV3 with GluonCV
GluonCV DeepLabV3 model on ADE20K dataset
model = gluoncv.model_zoo.get_model('deeplab_resnet101_ade', pretrained=True)
This runs pretty slow on a machine with no GPU.
That is the best picture of me ever!
Semantic Segmentation: Fully Convolutional Networks
GluonCV FCN model on PASCAL VOC dataset
model = gluoncv.model_zoo.get_model(‘fcn_resnet101_voc ', pretrained=True)
It found me.
For NYC Dot and PennDot camera usage, you have to sign a developer agreement for a feed!
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