squeezenet_generic_detect_v2

generic object detection

Embedded, Detection, Caffe

curl -X PUT http://localhost:8080/services/generic_detect_v2 -d '{
 "description": "generic object detection service",
 "model": {
  "repository": "/opt/models/generic_detect_v2",
  "create_repository": true,
  "init":"https://deepdetect.com/models/init/embedded/images/detection/squeezenet_ssd_generic_detect_v2.tar.gz"
 },
 "mllib": "caffe",
 "type": "supervised",
 "parameters": {
  "input": {
   "connector": "image"
  }
 }
}'
curl -X POST 'http://localhost:8080/predict' -d '{
  "service": "generic_detect_v2",
  "parameters": {
    "input": {},
    "output": {
      "confidence_threshold": 0.5,
      "bbox": true
    },
    "mllib": {
      "gpu": true
    }
  },
  "data": [
    "/data/example.jpg"
  ]
}'
from dd_client import DD
host = 'localhost'
port = 8080
dd = DD(host,port)
dd.set_return_format(dd.RETURN_PYTHON)

parameters_input = {}
parameters_mllib = {}
parameters_output = {"confidence_threshold":0.5,"bbox":True}
data = ["/data/example.jpg"]
sname = 'generic_detect_v2'
classif = dd.post_predict(sname,data,parameters_input,parameters_mllib,parameters_output)

// https://www.npmjs.com/package/deepdetect-js
var DD = require('deepdetect-js');

const dd = new DD({
  host: 'localhost',
  port: 8080
})

const postData = {
  "service": "generic_detect_v2",
  "parameters": {
    "input": {},
    "output": {
      "confidence_threshold": 0.5,
      "bbox": true
    },
    "mllib": {
      "gpu": true
    }
  },
  "data": [
    "/data/example.jpg"
  ]
}

async function run() {
  const predict = await dd.postPredict(postData);
  console.log(predict);
}

run()
{
  "status": {
    "code": 200,
    "msg": "OK"
  },
  "head": {
    "method": "/predict",
    "service": "generic_detect_v2",
    "time": 50
  },
  "body": {
    "predictions": [
      {
        "classes": [
          {
            "prob": 0.7916921973228455,
            "bbox": {
              "xmax": 494.16363525390625,
              "ymax": 24.178077697753906,
              "ymin": 411.2965087890625,
              "xmin": 97.42479705810547
            },
            "cat": "1"
          },
          {
            "prob": 0.6092143058776855,
            "bbox": {
              "xmax": 411.5879211425781,
              "ymax": 83.2486801147461,
              "ymin": 146.58810424804688,
              "xmin": 306.9629211425781
            },
            "cat": "1"
          },
          {
            "prob": 0.5768523812294006,
            "bbox": {
              "xmax": 295.932861328125,
              "ymax": 227.992919921875,
              "ymin": 380.48736572265625,
              "xmin": 162.4053192138672
            },
            "cat": "1"
          },
          {
            "prob": 0.57443767786026,
            "last": true,
            "bbox": {
              "xmax": 621.4046020507812,
              "ymax": 170.29580688476562,
              "ymin": 416.793212890625,
              "xmin": 477.0945129394531
            },
            "cat": "1"
          }
        ],
        "uri": "/data/example.jpg"
      }
    ]
  }
}