curl -X PUT 'http://localhost:8080/services/ilsvrc_googlenet' -d '{
"description": "image classification service",
"model": {
"repository": "/opt/models/ilsvrc_googlenet",
"init": "https://deepdetect.com/models/init/desktop/images/classification/ilsvrc_googlenet.tar.gz",
"create_repository": true
},
"mllib": "caffe",
"type": "supervised",
"parameters": {
"input": {
"connector": "image"
}
}
}'
curl -X POST 'http://localhost:8080/predict' -d '{
"service": "ilsvrc_googlenet",
"parameters": {
"output": {
"best": 3
},
"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 = {"best":3}
parameters_mllib = {}
parameters_output = {}
data = ["/data/example.jpg"]
sname = 'ilsvrc_googlenet'
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": "ilsvrc_googlenet",
"parameters": {
"input": {
"best": 3
},
},
"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": "ilsvrc_googlenet",
"time": 201
},
"body": {
"predictions": [
{
"classes": [
{
"prob": 0.0911879912018776,
"last": true,
"cat": "ambulance"
}
],
"uri": "/data/example.jpg"
}
]
}
}
curl -X PUT http://localhost:8080/services/detection_600 -d '{
"description": "object detection service",
"model": {
"repository": "/opt/models/detection_600",
"create_repository": true,
"init":"https://deepdetect.com/models/init/desktop/images/detection/detection_600.tar.gz"
},
"parameters": {"input": {"connector":"image"}},
"mllib": "caffe",
"type": "supervised"
}'
curl -X POST 'http://localhost:8080/predict' -d '{
"service": "detection_600",
"parameters": {
"output": {
"confidence_threshold": 0.3,
"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":0.3,"bbox":True}
data = ["/data/example.jpg"]
sname = 'detection_600'
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": "detection_600",
"parameters": {
"output": {
"confidence_threshold": 0.3,
"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": "detection_600",
"time": 60
},
"body": {
"predictions": [
{
"classes": [
{
"prob": 0.8248323798179626,
"bbox": {
"xmax": 487.2122497558594,
"ymax": 351.72113037109375,
"ymin": 521.0411376953125,
"xmin": 301.8126220703125
},
"cat": "Car"
},
{
"prob": 0.29296377301216125,
"bbox": {
"xmax": 202.32383728027344,
"ymax": 352.185791015625,
"ymin": 447.30419921875,
"xmin": 12.260007858276367
},
"cat": "Car"
},
{
"prob": 0.14670416712760925,
"bbox": {
"xmax": 534.9172973632812,
"ymax": 8.029011726379395,
"ymin": 397.7722473144531,
"xmin": 17.81478500366211
},
"cat": "Tree"
},
{
"prob": 0.13783478736877441,
"bbox": {
"xmax": 288.7256774902344,
"ymax": 335.0918273925781,
"ymin": 404.08905029296875,
"xmin": 156.46469116210938
},
"cat": "Van"
},
{
"prob": 0.13422219455242157,
"last": true,
"bbox": {
"xmax": 156.6908416748047,
"ymax": 150.88865661621094,
"ymin": 346.5965881347656,
"xmin": 21.79889488220215
},
"cat": "Tree"
}
],
"uri": "/data/example.jpg"
}
]
}
}
curl -X PUT http://localhost:8080/services/faces -d '{
"description": "face detection service",
"model": {
"repository": "/opt/models/faces",
"create_repository": true,
"init":"https://deepdetect.com/models/init/desktop/images/detection/faces_512.tar.gz"
},
"mllib": "caffe",
"type": "supervised",
"parameters": {
"input": {
"connector": "image"
}
}
}'
curl -X POST 'http://localhost:8080/predict' -d '{
"service": "faces",
"parameters": {
"input": {},
"output": {
"confidence_threshold": 0.4,
"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": 0.4, "bbox": True}
data = ["/data/example.jpg"]
sname = 'faces'
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": "faces",
"parameters": {
"input": {}
"output": {
"confidence_threshold": 0.4,
"bbox": true
},
"mllib": {}
},
"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": "faces",
"time": 46
},
"body": {
"predictions": [
{
"classes": [
{
"prob": 0.390458345413208,
"bbox": {
"xmax": 364.34820556640625,
"ymax": 50.02314376831055,
"ymin": 82.06399536132812,
"xmin": 326.3580017089844
},
"cat": "1"
},
{
"prob": 0.3900381922721863,
"bbox": {
"xmax": 271.8947448730469,
"ymax": 47.45260238647461,
"ymin": 74.53034973144531,
"xmin": 239.1929931640625
},
"cat": "1"
},
{
"prob": 0.325770765542984,
"bbox": {
"xmax": 531.6181030273438,
"ymax": 57.574459075927734,
"ymin": 82.18014526367188,
"xmin": 501.7938232421875
},
"cat": "1"
},
{
"prob": 0.23247282207012177,
"bbox": {
"xmax": 230.73373413085938,
"ymax": 16.717960357666016,
"ymin": 38.75651931762695,
"xmin": 201.1503448486328
},
"cat": "1"
},
{
"prob": 0.21733301877975464,
"bbox": {
"xmax": 398.0325927734375,
"ymax": 38.843482971191406,
"ymin": 60.36002731323242,
"xmin": 371.2444152832031
},
"cat": "1"
},
{
"prob": 0.20370665192604065,
"bbox": {
"xmax": 439.99615478515625,
"ymax": 48.639259338378906,
"ymin": 74.54566955566406,
"xmin": 407.150390625
},
"cat": "1"
},
{
"prob": 0.1948963850736618,
"bbox": {
"xmax": 160.39971923828125,
"ymax": 27.83022689819336,
"ymin": 50.85374450683594,
"xmin": 132.84400939941406
},
"cat": "1"
},
{
"prob": 0.18383292853832245,
"bbox": {
"xmax": 536.7980346679688,
"ymax": 1.5278087854385376,
"ymin": 26.120481491088867,
"xmin": 472.716796875
},
"cat": "1"
},
{
"prob": 0.1603844314813614,
"last": true,
"bbox": {
"xmax": 88.8065185546875,
"ymax": 45.23637771606445,
"ymin": 68.2235107421875,
"xmin": 61.595584869384766
},
"cat": "1"
}
],
"uri": "/data/example.jpg"
}
]
}
}
curl -X PUT http://localhost:8080/services/faces_gender -d '{
"description": "face and gender detection service",
"model": {
"repository": "/opt/models/faces_gender",
"create_repository": true,
"init":"https://deepdetect.com/models/init/desktop/images/detection/faces_gender.tar.gz"
},
"mllib": "caffe",
"type": "supervised",
"parameters": {
"input": {
"connector": "image"
}
}
}'
curl -X POST 'http://localhost:8080/predict' -d '{
"service": "faces_gender",
"parameters": {
"input": {},
"output": {
"confidence_threshold": 0.4,
"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": 0.4, "bbox": True}
data = ["/data/example.jpg"]
sname = 'faces_gender'
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": "faces_gender",
"parameters": {
"input": {}
"output": {
"confidence_threshold": 0.4,
"bbox": true
},
"mllib": {}
},
"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": "faces_gender_v4",
"time": 379
},
"body": {
"predictions": [
{
"classes": [
{
"prob": 0.9989920258522034,
"bbox": {
"xmax": 188.1786346435547,
"ymax": 95.33650970458984,
"ymin": 253.5126190185547,
"xmin": 25.546676635742188
},
"cat": "male"
},
{
"prob": 0.9103379845619202,
"bbox": {
"xmax": 398.0808410644531,
"ymax": 98.45462799072266,
"ymin": 257.0015869140625,
"xmin": 227.84500122070312
},
"cat": "male"
},
{
"prob": 0.8768270611763,
"bbox": {
"xmax": 604.056884765625,
"ymax": 96.18730926513672,
"ymin": 257.2901306152344,
"xmin": 434.84747314453125
},
"cat": "male"
},
{
"prob": 0.16960744559764862,
"last": true,
"bbox": {
"xmax": 599.8035888671875,
"ymax": 92.09048461914062,
"ymin": 260.4921569824219,
"xmin": 430.36224365234375
},
"cat": "female"
}
],
"uri": "/data/example.jpg"
}
]
}
}
curl -X PUT http://localhost:8080/services/faces_emo -d '{
"description": "face emotion detection service",
"model": {
"repository": "/opt/models/faces_emo",
"create_repository": true,
"init":"https://deepdetect.com/models/init/desktop/images/detection/faces_emo.tar.gz"
},
"mllib": "caffe",
"type": "supervised",
"parameters": {
"input": {
"connector": "image"
}
}
}'
curl -X POST 'http://localhost:8080/predict' -d '{
"service": "faces_emo",
"parameters": {
"input": {},
"output": {
"confidence_threshold": 0.4,
"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": 0.4, "bbox": True}
data = ["/data/example.jpg"]
sname = 'faces_emo'
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": "faces_emo",
"parameters": {
"input": {}
"output": {
"confidence_threshold": 0.4,
"bbox": true
},
"mllib": {}
},
"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": "faces_emo",
"time": 33
},
"body": {
"predictions": [
{
"classes": [
{
"prob": 0.851776659488678,
"bbox": {
"xmax": 322.82733154296875,
"ymax": 40.19896697998047,
"ymin": 132.21400451660156,
"xmin": 236.5441436767578
},
"cat": "neutral"
},
{
"prob": 0.8290548920631409,
"bbox": {
"xmax": 146.94943237304688,
"ymax": 35.533573150634766,
"ymin": 132.1978302001953,
"xmin": 59.38333511352539
},
"cat": "neutral"
},
{
"prob": 0.7160714268684387,
"last": true,
"bbox": {
"xmax": 321.46368408203125,
"ymax": 294.2980651855469,
"ymin": 390.796875,
"xmin": 235.3292694091797
},
"cat": "neutral"
}
],
"uri": "/data/example.jpg"
}
]
}
}
curl -X PUT 'http://localhost:8080/services/age_real' -d '{
"description": "age estimation service",
"model": {
"repository": "/opt/models/age_real",
"init": "https://deepdetect.com/models/init/desktop/images/classification/age_real.tar.gz",
"create_repository":true
},
"mllib": "caffe",
"type": "supervised",
"parameters": {
"input": {
"connector": "image"
}
}
}'
curl -X POST 'http://localhost:8080/predict' -d '{
"service": "age_real",
"parameters": {
"input": {},
"output": {
"confidence_threshold": 0.05,
"best": 1
},
"mllib": {
}
},
"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 = {"confidence_threshold":0.05, "best":1}
parameters_mllib = {}
parameters_output = {}
data = ["/data/example.jpg"]
sname = 'age_real'
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": "age_real",
"parameters": {
"input": {
"confidence_threshold": 0.05,
"best": 1
},
"output": {},
"mllib": {}
},
"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": "age_real",
"time": 993
},
"body": {
"predictions": [
{
"classes": [
{
"prob": 0.0911879912018776,
"last": true,
"cat": "24"
}
],
"uri": "/data/example.jpg"
}
]
}
}
curl -X PUT 'http://localhost:8080/services/basic_fashion' -d '{
"description": "clothes detection service",
"model": {
"repository": "/opt/platform/models/basic_fashion",
"init": "https://www.deepdetect.com/models/init/desktop/images/detection/basic_fashion_v2.tar.gz",
"create_repository":true
},
"mllib": "caffe",
"type": "supervised",
"parameters": {
"input": {
"connector": "image"
}
}
}'
curl -X POST 'http://localhost:8080/predict' -d '{
"service": "basic_fashion",
"parameters": {
"input": {},
"output": {
"confidence_threshold": 0.4,
"bbox": 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_output = {"confidence_threshold":0.4, "bbox":True}
data = ["/data/example.jpg"]
sname = 'basic_fashion'
classif = dd.post_predict(sname,data,{},{},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": "basic_fashion",
"parameters": {
"output": {
"confidence_threshold": 0.4,
"bbox": 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": "basic_fashion_v2",
"time": 103
},
"body": {
"predictions": [
{
"classes": [
{
"prob": 0.283363938331604,
"bbox": {
"xmax": 145.92205810546875,
"ymax": 173.904541015625,
"ymin": 348.5083923339844,
"xmin": 9.65214729309082
},
"cat": "bag"
},
{
"prob": 0.2829309105873108,
"bbox": {
"xmax": 285.46868896484375,
"ymax": 15.518260955810547,
"ymin": 63.17625427246094,
"xmin": 220.40829467773438
},
"cat": "hat"
},
{
"prob": 0.1963716745376587,
"bbox": {
"xmax": 270.4206848144531,
"ymax": 60.39577102661133,
"ymin": 78.02435302734375,
"xmin": 232.13482666015625
},
"cat": "glasses"
},
{
"prob": 0.1955023854970932,
"last": true,
"bbox": {
"xmax": 313.3597106933594,
"ymax": 144.37893676757812,
"ymin": 412.1800231933594,
"xmin": 197.72695922851562
},
"cat": "bag"
}
],
"uri": "/data/example.jpg"
}
]
}
}
curl -X PUT http://localhost:8080/services/classification_21k -d '{
"description": "generic image classification service",
"model": {
"repository": "/opt/models/classification_21k",
"init":"https://deepdetect.com/models/init/desktop/images/classification/classification_21k.tar.gz",
"create_repository": true
},
"mllib": "caffe",
"type": "supervised",
"parameters": {
"input": {
"connector": "image"
}
}
}'
curl -X POST 'http://localhost:8080/predict' -d '{
"service": "classification_21k",
"parameters": {
"input": {},
"output": {
"confidence_threshold": 0.4,
"best": 3
},
"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": 0.4, "best": 3}
data = ["/data/example.jpg"]
sname = 'classification_21k'
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": "classification_21k",
"parameters": {
"input": {},
"output": {
"confidence_threshold": 0.4,
"best": 3
},
"mllib": {}
},
"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": "classification_21k",
"time": 41
},
"body": {
"predictions": [
{
"classes": [
{
"prob": 0.073883056640625,
"cat": "Atrium"
},
{
"prob": 0.06305904686450958,
"cat": "Library"
},
{
"prob": 0.06247774139046669,
"cat": "Roof"
},
{
"prob": 0.03711871802806854,
"cat": "Proton accelerator"
}
],
"uri": "/data/example.jpg"
}
]
}
}
curl -X PUT http://localhost:8080/services/classification_5k -d '{
"description": "generic image classification service",
"model": {
"repository": "/opt/models/classification_5k",
"init":"https://deepdetect.com/models/init/desktop/images/classification/classification_5k.tar.gz",
"create_repository": true
},
"mllib": "tensorflow",
"type": "supervised",
"parameters": {
"input": {
"connector": "image"
}
}
}'
curl -X POST 'http://localhost:8080/predict' -d '{
"service": "classification_5k",
"parameters": {
"input": {},
"output": {
"confidence_threshold": 0.4,
"best": 3
},
"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": 0.4, "best": 3}
data = ["/data/example.jpg"]
sname = 'classification_5k'
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": "classification_5k",
"parameters": {
"input": {},
"output": {
"confidence_threshold": 0.4,
"best": 3
},
"mllib": {}
},
"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": "classification_5k",
"time": 41
},
"body": {
"predictions": [
{
"classes": [
{
"prob": 0.06305904686450958,
"cat": "Library"
},
{
"prob": 0.06247774139046669,
"cat": "Roof"
}
],
"uri": "/data/example.jpg"
}
]
}
}
curl -X PUT http://localhost:8080/services/faces_embedded -d '{
"description": "Faces embedded",
"model": {
"repository": "/opt/models/faces_embedded",
"create_repository": true,
"init":"https://deepdetect.com/models/init/embedded/images/detection/squeezenet_ssd_faces.tar.gz"
},
"mllib": "caffe",
"type": "supervised",
"parameters": {
"input": {
"connector": "image"
}
}
}'
curl -X POST 'http://localhost:8080/predict' -d '{
"service": "faces_embedded",
"parameters": {
"input": {},
"output": {
"confidence_threshold": 0.3,
"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 = {"confidence_threshold":0.3, "bbox":True}
parameters_mllib = {}
parameters_output = {}
data = ["/data/example.jpg"]
sname = 'faces_embedded'
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": "faces_embedded",
"parameters": {
"input": {
"confidence_threshold": 0.3,
"bbox": true
},
"output": {},
"mllib": {}
},
"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": "faces_embedded",
"time": 17
},
"body": {
"predictions": [
{
"classes": [
{
"prob": 1,
"last": true,
"cat": "believed"
}
],
"uri": "/data/example.jpg"
}
]
}
}
curl -X PUT http://localhost:8080/services/faces_embedded -d '{
"description": "Faces embedded",
"model": {
"repository": "/opt/models/faces_embedded",
"create_repository": true,
"init":"https://deepdetect.com/models/init/embedded/images/detection/squeezenet_ssd_faces_ncnn.tar.gz"
},
"mllib": "ncnn",
"type": "supervised",
"parameters": {
"input": {
"connector": "image"
}
}
}'
curl -X POST 'http://localhost:8080/predict' -d '{
"service": "faces_embedded",
"parameters": {
"input": {},
"output": {
"confidence_threshold": 0.3,
"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 = {"confidence_threshold":0.3, "bbox":True}
parameters_mllib = {}
parameters_output = {}
data = ["/data/example.jpg"]
sname = 'faces_embedded'
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": "faces_embedded",
"parameters": {
"input": {
"confidence_threshold": 0.3,
"bbox": true
},
"output": {},
"mllib": {}
},
"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": "faces_embedded",
"time": 17
},
"body": {
"predictions": [
{
"classes": [
{
"prob": 1,
"last": true,
"cat": "believed"
}
],
"uri": "/data/example.jpg"
}
]
}
}
curl -X PUT http://localhost:8080/services/nsfw -d '{
"description": "nsfw classification service",
"model": {
"repository": "/opt/models/nsfw",
"create_repository": true,
"init":"https://deepdetect.com/models/init/desktop/images/classification/nsfw.tar.gz"
},
"mllib": "caffe",
"type": "supervised",
"parameters": {
"input": {
"connector": "image"
}
}
}'
curl -X POST 'http://localhost:8080/predict' -d '{
"service": "nsfw",
"parameters": {
"input": {},
"output": {
"confidence_threshold": 0.1
},
"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":0.1}
data = ["/data/example.jpg"]
sname = 'nsfw'
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": "nsfw",
"parameters": {
"input": {},
"output": {
"confidence_threshold": 0.1,
},
"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": "nsfw",
"time": 45
},
"body": {
"predictions": [
{
"classes": [
{
"prob": 0.8330243825912476,
"cat": "ok"
},
{
"prob": 0.16697560250759125,
"last": true,
"cat": "nsfw"
}
],
"uri": "/data/example.jpg"
}
]
}
}
curl -X PUT http://localhost:8080/services/places -d '{
"description": "places classification service",
"model": {
"repository": "/opt/models/places",
"create_repository": true,
"init":"https://deepdetect.com/models/init/desktop/images/classification/places.tar.gz"
},
"mllib": "caffe",
"type": "supervised",
"parameters": {
"input": {
"connector": "image"
}
}
}'
curl -X POST 'http://localhost:8080/predict' -d '{
"service": "places",
"parameters": {
"input": {},
"output": {
"confidence_threshold": 0.5
},
"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}
data = ["/data/example.jpg"]
sname = 'places'
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": "places",
"parameters": {
"input": {},
"output": {
"confidence_threshold": 0.5
},
"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": "places",
"time": 56
},
"body": {
"predictions": [
{
"classes": [
{
"prob": 0.559813380241394,
"cat": "downtown"
},
{
"prob": 0.21302251517772675,
"cat": "skyscraper"
},
{
"prob": 0.04305418208241463,
"cat": "office_building"
},
{
"prob": 0.030694598332047462,
"last": true,
"cat": "plaza"
}
],
"uri": "/opt/platform/data/alx/deepdetect.com/places.jpg"
}
]
}
}
curl -X PUT http://localhost:8080/services/segmentation_150 -d '{
"description": "object segmentation service",
"model": {
"repository": "/opt/models/segmentation_150",
"create_repository": true,
"init":"https://deepdetect.com/models/init/desktop/images/segmentation/segmentation_150.tar.gz"
},
"mllib": "caffe",
"type": "supervised",
"parameters": {
"input": {
"connector": "image"
}
}
}'
curl -X POST 'http://localhost:8080/predict' -d '{
"service": "segmentation_150",
"parameters": {
"input": {
"segmentation": true
},
"output": {},
"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 = {"segmentation":True}
parameters_mllib = {}
parameters_output = {}
data = ["/data/example.jpg"]
sname = 'segmentation_150'
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": "segmentation_150",
"parameters": {
"input": {
"segmentation": true
},
"output": {},
"mllib": {}
},
"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": "segmentation_150",
"time": 1297
},
"body": {
"predictions": [
{
"last": true,
"imgsize": {
"width": 639,
"height": 421
},
"vals": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
"...",
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"uri": "/data/example.jpg"
}
]
}
}
curl -X PUT http://localhost:8080/services/sent_en -d '{
"description": "English sentiment",
"model": {
"repository": "/opt/models/sent_en",
"create_repository": true,
"init":"https://deepdetect.com/models/init/desktop/text/sent_en_vdcnn.tar.gz"
},
"mllib": "caffe",
"type": "supervised",
"parameters": {
"input": {
"connector": "txt"
}
}
}'
curl -X POST 'http://localhost:8080/predict' -d '{
"service": "sent_en",
"parameters": {
"input": {},
"output": {
"confidence_threshold": 0.3,
"bbox": true
},
"mllib": {
"gpu": true
}
},
"data": [
"good stuff!"
]
}'
from dd_client import DD
host = 'localhost'
port = 8080
dd = DD(host,port)
dd.set_return_format(dd.RETURN_PYTHON)
parameters_input = {"confidence_threshold":0.3}
parameters_mllib = {}
parameters_output = {}
data = ["good stuff!"]
sname = 'sent_en'
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": "sent_en",
"parameters": {
"input": {
},
"output": {
"confidence_threshold": 0.3
},
"mllib": {
"gpu": true
}
},
"data": [
"good stuff!"
]
}
async function run() {
const predict = await dd.postPredict(postData);
console.log(predict);
}
run()
{
"status": {
"code": 200,
"msg": "OK"
},
"head": {
"method": "/predict",
"service": "sent_en",
"time": 17
},
"body": {
"predictions": [
{
"classes": [
{
"prob": 0.897,
"last": true,
"cat": "positive"
}
],
"uri": "0"
}
]
}
}
curl -X PUT http://localhost:8080/services/shufflenet -d '{
"description": "Shufflenet",
"model": {
"repository": "/opt/models/shufflenet",
"create_repository": true,
"init":"https://deepdetect.com/models/init/embedded/images/classification/shufflenet.tar.gz"
},
"mllib": "caffe",
"type": "supervised",
"parameters": {
"input": {
"connector": "image"
}
}
}'
curl -X POST 'http://localhost:8080/predict' -d '{
"service": "shufflenet",
"parameters": {
"input": {},
"output": {
"confidence_threshold": 0.3,
"best": 3
},
"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 = {"confidence_threshold":0.3, "best": 3}
parameters_mllib = {}
parameters_output = {}
data = ["/data/example.jpg"]
sname = 'shufflenet'
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": "shufflenet",
"parameters": {
"input": {
"confidence_threshold": 0.3,
"best": 3
},
"output": {},
"mllib": {}
},
"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": "shufflenet",
"time": 17
},
"body": {
"predictions": [
{
"classes": [
{
"prob": 0.073883056640625,
"cat": "Atrium"
},
{
"prob": 0.06305904686450958,
"cat": "Library"
},
{
"prob": 0.06247774139046669,
"cat": "Roof"
},
{
"prob": 0.03711871802806854,
"cat": "Proton accelerator"
}
],
"uri": "/data/example.jpg"
}
]
}
}
curl -X PUT http://localhost:8080/services/squeezenet -d '{
"description": "Squeezenet",
"model": {
"repository": "/opt/models/squeezenet",
"create_repository": true,
"init":"https://deepdetect.com/models/init/embedded/images/classification/squeezenet_v1.tar.gz"
},
"mllib": "caffe",
"type": "supervised",
"parameters": {
"input": {
"connector": "image"
}
}
}'
curl -X POST 'http://localhost:8080/predict' -d '{
"service": "squeezenet",
"parameters": {
"input": {},
"output": {
"confidence_threshold": 0.3,
"best": 3
},
"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 = {"confidence_threshold":0.3, "best": 3}
parameters_mllib = {}
parameters_output = {}
data = ["/data/example.jpg"]
sname = 'squeezenet'
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": "squeezenet",
"parameters": {
"input": {
"confidence_threshold": 0.3,
"best": 3
},
"output": {},
"mllib": {}
},
"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": "squeezenet",
"time": 17
},
"body": {
"predictions": [
{
"classes": [
{
"prob": 0.073883056640625,
"cat": "Atrium"
},
{
"prob": 0.06305904686450958,
"cat": "Library"
},
{
"prob": 0.06247774139046669,
"cat": "Roof"
},
{
"prob": 0.03711871802806854,
"cat": "Proton accelerator"
}
],
"uri": "/data/example.jpg"
}
]
}
}
curl -X PUT http://localhost:8080/services/squeezenet_ssd_voc -d '{
"description": "Squeezenet SSD",
"model": {
"repository": "/opt/models/squeezenet_ssd_voc",
"create_repository": true,
"init":"https://deepdetect.com/models/init/embedded/images/detection/squeezenet_ssd_voc.tar.gz"
},
"mllib": "caffe",
"type": "supervised",
"parameters": {
"input": {
"connector": "image"
}
}
}'
curl -X POST 'http://localhost:8080/predict' -d '{
"service": "squeezenet_ssd_voc",
"parameters": {
"input": {},
"output": {
"confidence_threshold": 0.3,
"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 = {"confidence_threshold":0.3, "bbox":True}
parameters_mllib = {}
parameters_output = {}
data = ["/data/example.jpg"]
sname = 'squeezenet_ssd_voc'
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": "squeezenet_ssd_voc",
"parameters": {
"input": {
"confidence_threshold": 0.3,
"bbox": true
},
"output": {},
"mllib": {}
},
"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": "squeezenet_ssd_voc",
"time": 17
},
"body": {
"predictions": [
{
"classes": [
{
"prob": 1,
"last": true,
"cat": "believed"
}
],
"uri": "/data/example.jpg"
}
]
}
}
curl -X PUT http://localhost:8080/services/squeezenet_ssd_voc -d '{
"description": "Squeezenet SSD",
"model": {
"repository": "/opt/models/squeezenet_ssd_voc",
"create_repository": true,
"init":"https://deepdetect.com/models/init/embedded/images/detection/squeezenet_ssd_voc_ncnn.tar.gz"
},
"mllib": "ncnn",
"type": "supervised",
"parameters": {
"input": {
"connector": "image"
}
}
}'
curl -X POST 'http://localhost:8080/predict' -d '{
"service": "squeezenet_ssd_voc",
"parameters": {
"input": {},
"output": {
"confidence_threshold": 0.3,
"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 = {"confidence_threshold":0.3, "bbox":True}
parameters_mllib = {}
parameters_output = {}
data = ["/data/example.jpg"]
sname = 'squeezenet_ssd_voc'
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": "squeezenet_ssd_voc",
"parameters": {
"input": {
"confidence_threshold": 0.3,
"bbox": true
},
"output": {},
"mllib": {}
},
"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": "squeezenet_ssd_voc",
"time": 17
},
"body": {
"predictions": [
{
"classes": [
{
"prob": 1,
"last": true,
"cat": "believed"
}
],
"uri": "/data/example.jpg"
}
]
}
}
curl -X PUT http://localhost:8080/services/vgg16 -d
{
"description": "generic image classification service",
"model": {
"repository": "/opt",
"init":"https://deepdetect.com/models/init/desktop/images/classification/vgg16.tar.gz",
"create_repository": true,
},
"mllib": "caffe",
"type": "supervised",
"parameters": {
"input": {
"connector": "image"
}
}
}
curl -X POST 'http://localhost:8080/predict' -d '{
"service": "vgg16",
"parameters": {
"input": {},
"output": {
"confidence_threshold": 0.4,
"best": 3
},
"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": 0.4, "best": 3}
data = ["/data/example.jpg"]
sname = 'vgg16'
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": "vgg16",
"parameters": {
"input": {},
"output": {
"confidence_threshold": 0.4,
"best": 3
},
"mllib": {}
},
"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": "vgg16",
"time": 41
},
"body": {
"predictions": [
{
"classes": [
{
"prob": 0.073883056640625,
"cat": "Atrium"
},
{
"prob": 0.06305904686450958,
"cat": "Library"
},
{
"prob": 0.06247774139046669,
"cat": "Roof"
},
{
"prob": 0.03711871802806854,
"cat": "Proton accelerator"
}
],
"uri": "/data/example.jpg"
}
]
}
}
Multiple words OCR, use `word_detect` model to first text in images and pass crops to this model
Desktop, Ocr, Caffe
curl -X PUT http://localhost:8080/services/word_ocr -d '{
"description": "Word ocr",
"model": {
"repository": "/opt/models/multiword_ocr",
"create_repository": true,
"init":"https://deepdetect.com/models/init/desktop/images/ocr/multiword_ocr.tar.gz"
},
"mllib": "caffe",
"type": "supervised",
"parameters": {
"input": {
"connector": "image"
}
}
}'
curl -X POST 'http://localhost:8080/predict' -d '{
"service": "word_ocr",
"parameters": {
"input": {},
"output": {
"confidence_threshold": 0,
"ctc": true,
"blank_label": 0
},
"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 = {"confidence_threshold":0, "ctc":True, "blank_label": 0}
parameters_mllib = {}
parameters_output = {}
data = ["/data/example.jpg"]
sname = 'word_ocr'
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": "word_ocr",
"parameters": {
"input": {
"confidence_threshold": 0,
"ctc": true,
"blank_label": 0
},
"output": {},
"mllib": {}
},
"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": "word_ocr",
"time": 17
},
"body": {
"predictions": [
{
"classes": [
{
"prob": 1,
"last": true,
"cat": "believed"
}
],
"uri": "/data/example.jpg"
}
]
}
}
curl -X PUT http://localhost:8080/services/words_mnist -d '{
"description": "OCR service",
"model": {
"repository": "/opt/models/words_mnist",
"create_repository": true,
"init":"https://deepdetect.com/models/init/desktop/images/ocr/words_mnist.tar.gz"
},
"mllib": "caffe",
"type": "supervised",
"parameters": {
"input": {
"connector": "image"
}
}
}'
curl -X POST 'http://localhost:8080/predict' -d '{
"service": "words_mnist",
"parameters": {
"input": {},
"output": {
"confidence_threshold": 0,
"ctc": true,
"blank_label": 0
},
"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 = {"confidence_threshold":0, "ctc":True, "blank_label": 0}
parameters_mllib = {}
parameters_output = {}
data = ["/data/example.jpg"]
sname = 'words_mnist'
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": "words_mnist",
"parameters": {
"input": {
"confidence_threshold": 0,
"ctc": true,
"blank_label": 0
},
"output": {},
"mllib": {}
},
"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": "words_mnist",
"time": 17
},
"body": {
"predictions": [
{
"classes": [
{
"prob": 1,
"last": true,
"cat": "believed"
}
],
"uri": "/data/example.jpg"
}
]
}
}
curl -X PUT http://localhost:8080/services/amazon_en -d '{
"description": "Product sentiment",
"model": {
"repository": "/opt/models/amazon_en",
"create_repository": true,
"init":"https://deepdetect.com/models/init/desktop/text/amazon_polarity_vdcnn.tar.gz"
},
"mllib": "caffe",
"type": "supervised",
"parameters": {
"input": {
"connector": "txt"
}
}
}'
curl -X POST 'http://localhost:8080/predict' -d '{
"service": "amazon_en",
"parameters": {
"input": {},
"output": {
"confidence_threshold": 0.3,
"bbox": true
},
"mllib": {
"gpu": true
}
},
"data": [
"good stuff!"
]
}'
from dd_client import DD
host = 'localhost'
port = 8080
dd = DD(host,port)
dd.set_return_format(dd.RETURN_PYTHON)
parameters_input = {"confidence_threshold":0.3}
parameters_mllib = {}
parameters_output = {}
data = ["good stuff!"]
sname = 'amazon_en'
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": "amazon_en",
"parameters": {
"input": {
},
"output": {
"confidence_threshold": 0.3
},
"mllib": {
"gpu": true
}
},
"data": [
"good stuff!"
]
}
async function run() {
const predict = await dd.postPredict(postData);
console.log(predict);
}
run()
{
"status": {
"code": 200,
"msg": "OK"
},
"head": {
"method": "/predict",
"service": "amazon_en",
"time": 17
},
"body": {
"predictions": [
{
"classes": [
{
"prob": 0.897,
"last": true,
"cat": "positive"
}
],
"uri": "0"
}
]
}
}
curl -X PUT http://localhost:8080/services/word_detect -d '{
"description": "Word detection",
"model": {
"repository": "/opt/models/word_detect",
"create_repository": true,
"init":"https://deepdetect.com/models/init/desktop/images/detection/word_detect_v2.tar.gz"
},
"mllib": "caffe",
"type": "supervised",
"parameters": {
"input": {
"connector": "image"
}
}
}'
curl -X POST 'http://localhost:8080/predict' -d '{
"service": "word_detect",
"parameters": {
"input": {},
"output": {
"confidence_threshold": 0.3,
"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 = {"confidence_threshold":0.3,"bbox":True}
parameters_mllib = {}
parameters_output = {}
data = ["/data/example.jpg"]
sname = 'word_detect'
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": "word_detect",
"parameters": {
"input": {
"confidence_threshold": 0,
},
"output": {
"confidence_threshold": 0.3,
"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": "word_detect",
"time": 17
},
"body": {
"predictions": [
{
"classes": [
{
"prob": 1,
"last": true,
"cat": "believed"
}
],
"uri": "/data/example.jpg"
}
]
}
}
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/desktop/images/detection/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"
}
]
}
}
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"
}
]
}
}
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_generic_detect_v2.tar.gz"
},
"mllib": "ncnn",
"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"
}
]
}
}
curl -X PUT http://localhost:8080/services/detection_201 -d '{
"description": "object detection service",
"model": {
"repository": "/opt/models/detection_201_simsearch",
"create_repository": true,
"init":"https://deepdetect.com/models/init/desktop/images/detection/detection_201_simsearch.tar.gz"
},
"parameters": {"input": {"connector":"image"}},
"mllib": "caffe",
"type": "supervised"
}'
curl -X PUT http://localhost:8080/services -d
{
}
curl -X POST 'http://localhost:8080/predict' -d '{
"service": "detection_201",
"parameters": {
"output": {
"confidence_threshold": 0.1,
"rois": "rois"
},
"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":0.1,"rois":"rois"}
data = ["/data/example.jpg"]
sname = 'detection_201'
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": "detection_201",
"parameters": {
"output": {
"confidence_threshold": 0.1,
"rois": "rois"
},
"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": "detection_201",
"time": 60
},
"body": {
"predictions": [
{
"classes": [
{
"prob": 0.8248323798179626,
"bbox": {
"xmax": 487.2122497558594,
"ymax": 351.72113037109375,
"ymin": 521.0411376953125,
"xmin": 301.8126220703125
},
"cat": "Car"
},
{
"prob": 0.29296377301216125,
"bbox": {
"xmax": 202.32383728027344,
"ymax": 352.185791015625,
"ymin": 447.30419921875,
"xmin": 12.260007858276367
},
"cat": "Car"
},
{
"prob": 0.14670416712760925,
"bbox": {
"xmax": 534.9172973632812,
"ymax": 8.029011726379395,
"ymin": 397.7722473144531,
"xmin": 17.81478500366211
},
"cat": "Tree"
},
{
"prob": 0.13783478736877441,
"bbox": {
"xmax": 288.7256774902344,
"ymax": 335.0918273925781,
"ymin": 404.08905029296875,
"xmin": 156.46469116210938
},
"cat": "Van"
},
{
"prob": 0.13422219455242157,
"last": true,
"bbox": {
"xmax": 156.6908416748047,
"ymax": 150.88865661621094,
"ymin": 346.5965881347656,
"xmin": 21.79889488220215
},
"cat": "Tree"
}
],
"uri": "/data/example.jpg"
}
]
}
}