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Object Detection (Yolov3)

Yolov3 object detection

This container comes with 3 models:

Speed

Hardware Model Inference Time (Milliseconds)
CPU Accurate 500 (intel i-7)
CPU Accurate-600 500 (intel i-7)
CPU Fast 50 (intel i-7)
GPU Accurate 50 (NVIDIA 2080)
GPU Accurate-600 50 (NVIDIA 2080)
GPU Fast 23 (NVIDIA 2080)

This model's inference time increases sublinearly with the number of people.

Accuracy

Detection Accuracy
Accurate 57.9 mAP (COCO)
Fast 33.1 mAP (COCO)
Accurate-600 untested

GPU Consumption

Model Consumption (MB)
Accurate minimum ~500MB
Fast minimum ~100MB
Accurate-600 minimum ~500MB

Detectors

Model Description
Fast Tiny Yolo v3 - 80 object classes
Accurate-600 yolov3 - 600 object classes.
Accurate yolov3 - 80 object classes.

Fast

  • very quick object detection with lower accuracy
  • can be run with or without a gpu
  • average cpu inference time is 80 ms per frame, thats roughly 12 FPS!
  • average gpu inference time is 23 ms per frame, roughly 45 FPS!
  • 80 object classes

Accurate-600

  • accurate object detection with slower detection time
  • can be run with or without a gpu
  • average cpu inference time is 500 ms, roughly 2 FPS
  • average gpu inference time is 50ms, roughly 20 FPS
  • 600 object classes though with lower accuracy than the "Accurate" model

Accurate

  • most accurate object detection with slower detection time
  • can be run with or without a gpu
  • average cpu inference time is 500 ms, roughly 2 FPS
  • average gpu inference time is 50ms, roughly 20 FPS
  • 80 object classes

Run

#cpu
docker run --rm -ti \
-p 8080:8080 \
sugarkubes/yolo-object-detection:cpu

#gpu cuda 10.1
docker run --rm -ti \
-p 8080:8080 \
sugarkubes/yolo-object-detection:gpu-cuda-10.1

#gpu cuda 10.2
docker run --rm -ti \
-p 8080:8080 \
sugarkubes/yolo-object-detection:gpu-cuda-10.2

Routes

GET / GET /health GET /healthz

  • Responds with a 200 for healthcheck

POST /predict

  • Example:
curl -X POST \\
http://0.0.0.0:9090/predict \\
-H 'Content-Type: application/json' \\
-d '{ "url": "https://s3.us-west-1.wasabisys.com/public.sugarkubes/repos/sugar-cv/object-detection/friends.jpg" }'
  • Post parameters
{
  "nms": 0.5", # non-max supression
  "confidence": 0.5, # confidence score for each bounding boxe (must be above this to be returned)
  "draw": true, # draw bounding boxes on returned image
  "return_image": true, # use false for production/faster results
  "url": 'https://your-image.jpg', # use url or b64 image
  "b64": "", # base 64 encoded image
}

ENV Variables

Variable Default
PORT 8080
HOST 0.0.0.0
BASIC_AUTH_USERNAME ""
BASIC_AUTH_PASSWORD ""
INPUT_WIDTH 640
INPUT_HEIGHT 640
MODEL accurate
LOG false

Google Cloud Run Enabled

  • This container can immediately be deployed using Google's new Cloud Run serverless service (cpu inference only)
  • Its a cheap and quick way to get the model online

Response

  • response is top left and bottom right of bounding box.
{
  "data": [
      [
      "Person", #class name
      0.7623502612113953, # confidence
      25,   # x1
      206,  # y1
      224,  # x2
      684   # y2
    ],
    ... other objects
  ],
  "image_size": [1920, 1080],
  "inference_time": 431.462,
  "confidence":0.5,
  "nms": o.5,
}

Authentication

  • The container comes with the ability to support basic auth.
  • basic auth is disabled by default.
  • to turn it off, set BASIC_AUTH_USERNAME="" and BASIC_AUTH_PASSWORD=""
  • to turn it on, set BASIC_AUTH_USERNAME="root" and BASIC_AUTH_PASSWORD="your password"

About the container

  • python 3.5.2 (cpu)
  • python 3.5.1 (gpu)
  • tensorflow 1.13.1

Validated Hosts

  • intel i series and e-series x86 machines running ubuntu server 18.04 19.04 20.04
  • cuda 10.1, 10.2, 9.2
  • Tested on NVIDIA GTX 1080Ti, NVIDIA P1000, NVIDIA Quadro, NVIDIA 20180
  • mac (cpu)