@tensorflow-models/coco-ssd - npm Package Compare versions

Comparing version 2.2.2 to 2.2.3

dist/classes.js

@@ -19,2 +19,3 @@ "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
+exports.CLASSES = void 0;
 exports.CLASSES = {
@@ -21,0 +22,0 @@ 1: {

dist/coco-ssd.es2017.esm.min.js

 /**
  * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
+ * Copyright 2023 Google LLC. All Rights Reserved.
  * Licensed under the Apache License, Version 2.0 (the "License");
@@ -17,3 +17,3 @@ * you may not use this file except in compliance with the License.
  */
-!function(e,a){"object"==typeof exports&&"undefined"!=typeof module?a(exports,require("@tensorflow/tfjs-converter"),require("@tensorflow/tfjs-core")):"function"==typeof define&&define.amd?define(["exports","@tensorflow/tfjs-converter","@tensorflow/tfjs-core"],a):a((e=e||self).cocoSsd=e.cocoSsd||{},e.tf,e.tf)}(this,(function(e,a,m){"use strict";const i={1:{name:"/m/01g317",id:1,displayName:"person"},2:{name:"/m/0199g",id:2,displayName:"bicycle"},3:{name:"/m/0k4j",id:3,displayName:"car"},4:{name:"/m/04_sv",id:4,displayName:"motorcycle"},5:{name:"/m/05czz6l",id:5,displayName:"airplane"},6:{name:"/m/01bjv",id:6,displayName:"bus"},7:{name:"/m/07jdr",id:7,displayName:"train"},8:{name:"/m/07r04",id:8,displayName:"truck"},9:{name:"/m/019jd",id:9,displayName:"boat"},10:{name:"/m/015qff",id:10,displayName:"traffic light"},11:{name:"/m/01pns0",id:11,displayName:"fire hydrant"},13:{name:"/m/02pv19",id:13,displayName:"stop sign"},14:{name:"/m/015qbp",id:14,displayName:"parking meter"},15:{name:"/m/0cvnqh",id:15,displayName:"bench"},16:{name:"/m/015p6",id:16,displayName:"bird"},17:{name:"/m/01yrx",id:17,displayName:"cat"},18:{name:"/m/0bt9lr",id:18,displayName:"dog"},19:{name:"/m/03k3r",id:19,displayName:"horse"},20:{name:"/m/07bgp",id:20,displayName:"sheep"},21:{name:"/m/01xq0k1",id:21,displayName:"cow"},22:{name:"/m/0bwd_0j",id:22,displayName:"elephant"},23:{name:"/m/01dws",id:23,displayName:"bear"},24:{name:"/m/0898b",id:24,displayName:"zebra"},25:{name:"/m/03bk1",id:25,displayName:"giraffe"},27:{name:"/m/01940j",id:27,displayName:"backpack"},28:{name:"/m/0hnnb",id:28,displayName:"umbrella"},31:{name:"/m/080hkjn",id:31,displayName:"handbag"},32:{name:"/m/01rkbr",id:32,displayName:"tie"},33:{name:"/m/01s55n",id:33,displayName:"suitcase"},34:{name:"/m/02wmf",id:34,displayName:"frisbee"},35:{name:"/m/071p9",id:35,displayName:"skis"},36:{name:"/m/06__v",id:36,displayName:"snowboard"},37:{name:"/m/018xm",id:37,displayName:"sports ball"},38:{name:"/m/02zt3",id:38,displayName:"kite"},39:{name:"/m/03g8mr",id:39,displayName:"baseball bat"},40:{name:"/m/03grzl",id:40,displayName:"baseball glove"},41:{name:"/m/06_fw",id:41,displayName:"skateboard"},42:{name:"/m/019w40",id:42,displayName:"surfboard"},43:{name:"/m/0dv9c",id:43,displayName:"tennis racket"},44:{name:"/m/04dr76w",id:44,displayName:"bottle"},46:{name:"/m/09tvcd",id:46,displayName:"wine glass"},47:{name:"/m/08gqpm",id:47,displayName:"cup"},48:{name:"/m/0dt3t",id:48,displayName:"fork"},49:{name:"/m/04ctx",id:49,displayName:"knife"},50:{name:"/m/0cmx8",id:50,displayName:"spoon"},51:{name:"/m/04kkgm",id:51,displayName:"bowl"},52:{name:"/m/09qck",id:52,displayName:"banana"},53:{name:"/m/014j1m",id:53,displayName:"apple"},54:{name:"/m/0l515",id:54,displayName:"sandwich"},55:{name:"/m/0cyhj_",id:55,displayName:"orange"},56:{name:"/m/0hkxq",id:56,displayName:"broccoli"},57:{name:"/m/0fj52s",id:57,displayName:"carrot"},58:{name:"/m/01b9xk",id:58,displayName:"hot dog"},59:{name:"/m/0663v",id:59,displayName:"pizza"},60:{name:"/m/0jy4k",id:60,displayName:"donut"},61:{name:"/m/0fszt",id:61,displayName:"cake"},62:{name:"/m/01mzpv",id:62,displayName:"chair"},63:{name:"/m/02crq1",id:63,displayName:"couch"},64:{name:"/m/03fp41",id:64,displayName:"potted plant"},65:{name:"/m/03ssj5",id:65,displayName:"bed"},67:{name:"/m/04bcr3",id:67,displayName:"dining table"},70:{name:"/m/09g1w",id:70,displayName:"toilet"},72:{name:"/m/07c52",id:72,displayName:"tv"},73:{name:"/m/01c648",id:73,displayName:"laptop"},74:{name:"/m/020lf",id:74,displayName:"mouse"},75:{name:"/m/0qjjc",id:75,displayName:"remote"},76:{name:"/m/01m2v",id:76,displayName:"keyboard"},77:{name:"/m/050k8",id:77,displayName:"cell phone"},78:{name:"/m/0fx9l",id:78,displayName:"microwave"},79:{name:"/m/029bxz",id:79,displayName:"oven"},80:{name:"/m/01k6s3",id:80,displayName:"toaster"},81:{name:"/m/0130jx",id:81,displayName:"sink"},82:{name:"/m/040b_t",id:82,displayName:"refrigerator"},84:{name:"/m/0bt_c3",id:84,displayName:"book"},85:{name:"/m/01x3z",id:85,displayName:"clock"},86:{name:"/m/02s195",id:86,displayName:"vase"},87:{name:"/m/01lsmm",id:87,displayName:"scissors"},88:{name:"/m/0kmg4",id:88,displayName:"teddy bear"},89:{name:"/m/03wvsk",id:89,displayName:"hair drier"},90:{name:"/m/012xff",id:90,displayName:"toothbrush"}};class d{constructor(e,a){this.modelPath=a||`https://storage.googleapis.com/tfjs-models/savedmodel/${this.getPrefix(e)}/model.json`}getPrefix(e){return"lite_mobilenet_v2"===e?`ssd${e}`:`ssd_${e}`}async load(){this.model=await a.loadGraphModel(this.modelPath);const e=m.zeros([1,300,300,3],"int32"),i=await this.model.executeAsync(e);await Promise.all(i.map((e=>e.data()))),i.map((e=>e.dispose())),e.dispose()}async infer(e,a,i){const d=m.tidy((()=>(e instanceof m.Tensor||(e=m.browser.fromPixels(e)),m.expandDims(e)))),s=d.shape[1],n=d.shape[2],l=await this.model.executeAsync(d),t=l[0].dataSync(),o=l[1].dataSync();d.dispose(),m.dispose(l);const[p,r]=this.calculateMaxScores(t,l[0].shape[1],l[0].shape[2]),c=m.getBackend();"webgl"===m.getBackend()&&m.setBackend("cpu");const y=m.tidy((()=>{const e=m.tensor2d(o,[l[1].shape[1],l[1].shape[3]]);return m.image.nonMaxSuppression(e,p,a,i,i)})),N=y.dataSync();return y.dispose(),c!==m.getBackend()&&m.setBackend(c),this.buildDetectedObjects(n,s,o,p,N,r)}buildDetectedObjects(e,a,m,d,s,n){const l=s.length,t=[];for(let o=0;o<l;o++){const l=[];for(let e=0;e<4;e++)l[e]=m[4*s[o]+e];const p=l[0]*a,r=l[1]*e,c=l[2]*a,y=l[3]*e;l[0]=r,l[1]=p,l[2]=y-r,l[3]=c-p,t.push({bbox:l,class:i[n[s[o]]+1].displayName,score:d[s[o]]})}return t}calculateMaxScores(e,a,m){const i=[],d=[];for(let s=0;s<a;s++){let a=Number.MIN_VALUE,n=-1;for(let i=0;i<m;i++)e[s*m+i]>a&&(a=e[s*m+i],n=i);i[s]=a,d[s]=n}return[i,d]}async detect(e,a=20,m=.5){return this.infer(e,a,m)}dispose(){null!=this.model&&this.model.dispose()}}e.ObjectDetection=d,e.load=async function(e={}){if(null==m)throw new Error("Cannot find TensorFlow.js. If you are using a <script> tag, please also include @tensorflow/tfjs on the page before using this model.");const a=e.base||"lite_mobilenet_v2",i=e.modelUrl;if(-1===["mobilenet_v1","mobilenet_v2","lite_mobilenet_v2"].indexOf(a))throw new Error(`ObjectDetection constructed with invalid base model ${a}. Valid names are 'mobilenet_v1', 'mobilenet_v2' and 'lite_mobilenet_v2'.`);const s=new d(a,i);return await s.load(),s},e.version="2.2.2",Object.defineProperty(e,"__esModule",{value:!0})}));
+!function(e,a){"object"==typeof exports&&"undefined"!=typeof module?a(exports,require("@tensorflow/tfjs-converter"),require("@tensorflow/tfjs-core")):"function"==typeof define&&define.amd?define(["exports","@tensorflow/tfjs-converter","@tensorflow/tfjs-core"],a):a((e=e||self).cocoSsd=e.cocoSsd||{},e.tf,e.tf)}(this,(function(e,a,m){"use strict";const i={1:{name:"/m/01g317",id:1,displayName:"person"},2:{name:"/m/0199g",id:2,displayName:"bicycle"},3:{name:"/m/0k4j",id:3,displayName:"car"},4:{name:"/m/04_sv",id:4,displayName:"motorcycle"},5:{name:"/m/05czz6l",id:5,displayName:"airplane"},6:{name:"/m/01bjv",id:6,displayName:"bus"},7:{name:"/m/07jdr",id:7,displayName:"train"},8:{name:"/m/07r04",id:8,displayName:"truck"},9:{name:"/m/019jd",id:9,displayName:"boat"},10:{name:"/m/015qff",id:10,displayName:"traffic light"},11:{name:"/m/01pns0",id:11,displayName:"fire hydrant"},13:{name:"/m/02pv19",id:13,displayName:"stop sign"},14:{name:"/m/015qbp",id:14,displayName:"parking meter"},15:{name:"/m/0cvnqh",id:15,displayName:"bench"},16:{name:"/m/015p6",id:16,displayName:"bird"},17:{name:"/m/01yrx",id:17,displayName:"cat"},18:{name:"/m/0bt9lr",id:18,displayName:"dog"},19:{name:"/m/03k3r",id:19,displayName:"horse"},20:{name:"/m/07bgp",id:20,displayName:"sheep"},21:{name:"/m/01xq0k1",id:21,displayName:"cow"},22:{name:"/m/0bwd_0j",id:22,displayName:"elephant"},23:{name:"/m/01dws",id:23,displayName:"bear"},24:{name:"/m/0898b",id:24,displayName:"zebra"},25:{name:"/m/03bk1",id:25,displayName:"giraffe"},27:{name:"/m/01940j",id:27,displayName:"backpack"},28:{name:"/m/0hnnb",id:28,displayName:"umbrella"},31:{name:"/m/080hkjn",id:31,displayName:"handbag"},32:{name:"/m/01rkbr",id:32,displayName:"tie"},33:{name:"/m/01s55n",id:33,displayName:"suitcase"},34:{name:"/m/02wmf",id:34,displayName:"frisbee"},35:{name:"/m/071p9",id:35,displayName:"skis"},36:{name:"/m/06__v",id:36,displayName:"snowboard"},37:{name:"/m/018xm",id:37,displayName:"sports ball"},38:{name:"/m/02zt3",id:38,displayName:"kite"},39:{name:"/m/03g8mr",id:39,displayName:"baseball bat"},40:{name:"/m/03grzl",id:40,displayName:"baseball glove"},41:{name:"/m/06_fw",id:41,displayName:"skateboard"},42:{name:"/m/019w40",id:42,displayName:"surfboard"},43:{name:"/m/0dv9c",id:43,displayName:"tennis racket"},44:{name:"/m/04dr76w",id:44,displayName:"bottle"},46:{name:"/m/09tvcd",id:46,displayName:"wine glass"},47:{name:"/m/08gqpm",id:47,displayName:"cup"},48:{name:"/m/0dt3t",id:48,displayName:"fork"},49:{name:"/m/04ctx",id:49,displayName:"knife"},50:{name:"/m/0cmx8",id:50,displayName:"spoon"},51:{name:"/m/04kkgm",id:51,displayName:"bowl"},52:{name:"/m/09qck",id:52,displayName:"banana"},53:{name:"/m/014j1m",id:53,displayName:"apple"},54:{name:"/m/0l515",id:54,displayName:"sandwich"},55:{name:"/m/0cyhj_",id:55,displayName:"orange"},56:{name:"/m/0hkxq",id:56,displayName:"broccoli"},57:{name:"/m/0fj52s",id:57,displayName:"carrot"},58:{name:"/m/01b9xk",id:58,displayName:"hot dog"},59:{name:"/m/0663v",id:59,displayName:"pizza"},60:{name:"/m/0jy4k",id:60,displayName:"donut"},61:{name:"/m/0fszt",id:61,displayName:"cake"},62:{name:"/m/01mzpv",id:62,displayName:"chair"},63:{name:"/m/02crq1",id:63,displayName:"couch"},64:{name:"/m/03fp41",id:64,displayName:"potted plant"},65:{name:"/m/03ssj5",id:65,displayName:"bed"},67:{name:"/m/04bcr3",id:67,displayName:"dining table"},70:{name:"/m/09g1w",id:70,displayName:"toilet"},72:{name:"/m/07c52",id:72,displayName:"tv"},73:{name:"/m/01c648",id:73,displayName:"laptop"},74:{name:"/m/020lf",id:74,displayName:"mouse"},75:{name:"/m/0qjjc",id:75,displayName:"remote"},76:{name:"/m/01m2v",id:76,displayName:"keyboard"},77:{name:"/m/050k8",id:77,displayName:"cell phone"},78:{name:"/m/0fx9l",id:78,displayName:"microwave"},79:{name:"/m/029bxz",id:79,displayName:"oven"},80:{name:"/m/01k6s3",id:80,displayName:"toaster"},81:{name:"/m/0130jx",id:81,displayName:"sink"},82:{name:"/m/040b_t",id:82,displayName:"refrigerator"},84:{name:"/m/0bt_c3",id:84,displayName:"book"},85:{name:"/m/01x3z",id:85,displayName:"clock"},86:{name:"/m/02s195",id:86,displayName:"vase"},87:{name:"/m/01lsmm",id:87,displayName:"scissors"},88:{name:"/m/0kmg4",id:88,displayName:"teddy bear"},89:{name:"/m/03wvsk",id:89,displayName:"hair drier"},90:{name:"/m/012xff",id:90,displayName:"toothbrush"}};class d{constructor(e,a){this.modelPath=a||`https://storage.googleapis.com/tfjs-models/savedmodel/${this.getPrefix(e)}/model.json`}getPrefix(e){return"lite_mobilenet_v2"===e?`ssd${e}`:`ssd_${e}`}async load(){this.model=await a.loadGraphModel(this.modelPath);const e=m.zeros([1,300,300,3],"int32"),i=await this.model.executeAsync(e);await Promise.all(i.map((e=>e.data()))),i.map((e=>e.dispose())),e.dispose()}async infer(e,a,i){const d=m.tidy((()=>(e instanceof m.Tensor||(e=m.browser.fromPixels(e)),m.expandDims(e)))),s=d.shape[1],n=d.shape[2],l=await this.model.executeAsync(d),t=l[0].dataSync(),o=l[1].dataSync();d.dispose(),m.dispose(l);const[p,r]=this.calculateMaxScores(t,l[0].shape[1],l[0].shape[2]),c=m.getBackend();"webgl"===m.getBackend()&&m.setBackend("cpu");const y=m.tidy((()=>{const e=m.tensor2d(o,[l[1].shape[1],l[1].shape[3]]);return m.image.nonMaxSuppression(e,p,a,i,i)})),N=y.dataSync();return y.dispose(),c!==m.getBackend()&&m.setBackend(c),this.buildDetectedObjects(n,s,o,p,N,r)}buildDetectedObjects(e,a,m,d,s,n){const l=s.length,t=[];for(let o=0;o<l;o++){const l=[];for(let e=0;e<4;e++)l[e]=m[4*s[o]+e];const p=l[0]*a,r=l[1]*e,c=l[2]*a,y=l[3]*e;l[0]=r,l[1]=p,l[2]=y-r,l[3]=c-p,t.push({bbox:l,class:i[n[s[o]]+1].displayName,score:d[s[o]]})}return t}calculateMaxScores(e,a,m){const i=[],d=[];for(let s=0;s<a;s++){let a=Number.MIN_VALUE,n=-1;for(let i=0;i<m;i++)e[s*m+i]>a&&(a=e[s*m+i],n=i);i[s]=a,d[s]=n}return[i,d]}async detect(e,a=20,m=.5){return this.infer(e,a,m)}dispose(){null!=this.model&&this.model.dispose()}}e.ObjectDetection=d,e.load=async function(e={}){if(null==m)throw new Error("Cannot find TensorFlow.js. If you are using a <script> tag, please also include @tensorflow/tfjs on the page before using this model.");const a=e.base||"lite_mobilenet_v2",i=e.modelUrl;if(-1===["mobilenet_v1","mobilenet_v2","lite_mobilenet_v2"].indexOf(a))throw new Error(`ObjectDetection constructed with invalid base model ${a}. Valid names are 'mobilenet_v1', 'mobilenet_v2' and 'lite_mobilenet_v2'.`);const s=new d(a,i);return await s.load(),s},e.version="2.2.3",Object.defineProperty(e,"__esModule",{value:!0})}));
 //# sourceMappingURL=coco-ssd.es2017.esm.min.js.map

dist/coco-ssd.js

 /**
  * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
+ * Copyright 2023 Google LLC. All Rights Reserved.
  * Licensed under the Apache License, Version 2.0 (the "License");
@@ -497,3 +497,3 @@ * you may not use this file except in compliance with the License.
     // This code is auto-generated, do not modify this file!
-    var version = '2.2.2';
+    var version = '2.2.3';
 
@@ -533,3 +533,3 @@ /**
                             throw new Error("ObjectDetection constructed with invalid base model " +
-                                (base + ". Valid names are 'mobilenet_v1',") +
+                                "".concat(base, ". Valid names are 'mobilenet_v1',") +
                                 " 'mobilenet_v2' and 'lite_mobilenet_v2'.");
@@ -549,6 +549,6 @@ }
             this.modelPath =
-                modelUrl || "" + BASE_PATH + this.getPrefix(base) + "/model.json";
+                modelUrl || "".concat(BASE_PATH).concat(this.getPrefix(base), "/model.json");
         }
         ObjectDetection.prototype.getPrefix = function (base) {
-            return base === 'lite_mobilenet_v2' ? "ssd" + base : "ssd_" + base;
+            return base === 'lite_mobilenet_v2' ? "ssd".concat(base) : "ssd_".concat(base);
         };
@@ -555,0 +555,0 @@ ObjectDetection.prototype.load = function () {

dist/coco-ssd.min.js

 /**
  * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
+ * Copyright 2023 Google LLC. All Rights Reserved.
  * Licensed under the Apache License, Version 2.0 (the "License");
@@ -17,3 +17,3 @@ * you may not use this file except in compliance with the License.
  */
-!function(e,a){"object"==typeof exports&&"undefined"!=typeof module?a(exports,require("@tensorflow/tfjs-converter"),require("@tensorflow/tfjs-core")):"function"==typeof define&&define.amd?define(["exports","@tensorflow/tfjs-converter","@tensorflow/tfjs-core"],a):a((e=e||self).cocoSsd=e.cocoSsd||{},e.tf,e.tf)}(this,(function(e,a,i){"use strict";function n(e,a,i,n){return new(i||(i=Promise))((function(m,t){function d(e){try{o(n.next(e))}catch(e){t(e)}}function s(e){try{o(n.throw(e))}catch(e){t(e)}}function o(e){var a;e.done?m(e.value):(a=e.value,a instanceof i?a:new i((function(e){e(a)}))).then(d,s)}o((n=n.apply(e,a||[])).next())}))}function m(e,a){var i,n,m,t,d={label:0,sent:function(){if(1&m[0])throw m[1];return m[1]},trys:[],ops:[]};return t={next:s(0),throw:s(1),return:s(2)},"function"==typeof Symbol&&(t[Symbol.iterator]=function(){return this}),t;function s(t){return function(s){return function(t){if(i)throw new TypeError("Generator is already executing.");for(;d;)try{if(i=1,n&&(m=2&t[0]?n.return:t[0]?n.throw||((m=n.return)&&m.call(n),0):n.next)&&!(m=m.call(n,t[1])).done)return m;switch(n=0,m&&(t=[2&t[0],m.value]),t[0]){case 0:case 1:m=t;break;case 4:return d.label++,{value:t[1],done:!1};case 5:d.label++,n=t[1],t=[0];continue;case 7:t=d.ops.pop(),d.trys.pop();continue;default:if(!(m=d.trys,(m=m.length>0&&m[m.length-1])||6!==t[0]&&2!==t[0])){d=0;continue}if(3===t[0]&&(!m||t[1]>m[0]&&t[1]<m[3])){d.label=t[1];break}if(6===t[0]&&d.label<m[1]){d.label=m[1],m=t;break}if(m&&d.label<m[2]){d.label=m[2],d.ops.push(t);break}m[2]&&d.ops.pop(),d.trys.pop();continue}t=a.call(e,d)}catch(e){t=[6,e],n=0}finally{i=m=0}if(5&t[0])throw t[1];return{value:t[0]?t[1]:void 0,done:!0}}([t,s])}}}var t={1:{name:"/m/01g317",id:1,displayName:"person"},2:{name:"/m/0199g",id:2,displayName:"bicycle"},3:{name:"/m/0k4j",id:3,displayName:"car"},4:{name:"/m/04_sv",id:4,displayName:"motorcycle"},5:{name:"/m/05czz6l",id:5,displayName:"airplane"},6:{name:"/m/01bjv",id:6,displayName:"bus"},7:{name:"/m/07jdr",id:7,displayName:"train"},8:{name:"/m/07r04",id:8,displayName:"truck"},9:{name:"/m/019jd",id:9,displayName:"boat"},10:{name:"/m/015qff",id:10,displayName:"traffic light"},11:{name:"/m/01pns0",id:11,displayName:"fire hydrant"},13:{name:"/m/02pv19",id:13,displayName:"stop sign"},14:{name:"/m/015qbp",id:14,displayName:"parking meter"},15:{name:"/m/0cvnqh",id:15,displayName:"bench"},16:{name:"/m/015p6",id:16,displayName:"bird"},17:{name:"/m/01yrx",id:17,displayName:"cat"},18:{name:"/m/0bt9lr",id:18,displayName:"dog"},19:{name:"/m/03k3r",id:19,displayName:"horse"},20:{name:"/m/07bgp",id:20,displayName:"sheep"},21:{name:"/m/01xq0k1",id:21,displayName:"cow"},22:{name:"/m/0bwd_0j",id:22,displayName:"elephant"},23:{name:"/m/01dws",id:23,displayName:"bear"},24:{name:"/m/0898b",id:24,displayName:"zebra"},25:{name:"/m/03bk1",id:25,displayName:"giraffe"},27:{name:"/m/01940j",id:27,displayName:"backpack"},28:{name:"/m/0hnnb",id:28,displayName:"umbrella"},31:{name:"/m/080hkjn",id:31,displayName:"handbag"},32:{name:"/m/01rkbr",id:32,displayName:"tie"},33:{name:"/m/01s55n",id:33,displayName:"suitcase"},34:{name:"/m/02wmf",id:34,displayName:"frisbee"},35:{name:"/m/071p9",id:35,displayName:"skis"},36:{name:"/m/06__v",id:36,displayName:"snowboard"},37:{name:"/m/018xm",id:37,displayName:"sports ball"},38:{name:"/m/02zt3",id:38,displayName:"kite"},39:{name:"/m/03g8mr",id:39,displayName:"baseball bat"},40:{name:"/m/03grzl",id:40,displayName:"baseball glove"},41:{name:"/m/06_fw",id:41,displayName:"skateboard"},42:{name:"/m/019w40",id:42,displayName:"surfboard"},43:{name:"/m/0dv9c",id:43,displayName:"tennis racket"},44:{name:"/m/04dr76w",id:44,displayName:"bottle"},46:{name:"/m/09tvcd",id:46,displayName:"wine glass"},47:{name:"/m/08gqpm",id:47,displayName:"cup"},48:{name:"/m/0dt3t",id:48,displayName:"fork"},49:{name:"/m/04ctx",id:49,displayName:"knife"},50:{name:"/m/0cmx8",id:50,displayName:"spoon"},51:{name:"/m/04kkgm",id:51,displayName:"bowl"},52:{name:"/m/09qck",id:52,displayName:"banana"},53:{name:"/m/014j1m",id:53,displayName:"apple"},54:{name:"/m/0l515",id:54,displayName:"sandwich"},55:{name:"/m/0cyhj_",id:55,displayName:"orange"},56:{name:"/m/0hkxq",id:56,displayName:"broccoli"},57:{name:"/m/0fj52s",id:57,displayName:"carrot"},58:{name:"/m/01b9xk",id:58,displayName:"hot dog"},59:{name:"/m/0663v",id:59,displayName:"pizza"},60:{name:"/m/0jy4k",id:60,displayName:"donut"},61:{name:"/m/0fszt",id:61,displayName:"cake"},62:{name:"/m/01mzpv",id:62,displayName:"chair"},63:{name:"/m/02crq1",id:63,displayName:"couch"},64:{name:"/m/03fp41",id:64,displayName:"potted plant"},65:{name:"/m/03ssj5",id:65,displayName:"bed"},67:{name:"/m/04bcr3",id:67,displayName:"dining table"},70:{name:"/m/09g1w",id:70,displayName:"toilet"},72:{name:"/m/07c52",id:72,displayName:"tv"},73:{name:"/m/01c648",id:73,displayName:"laptop"},74:{name:"/m/020lf",id:74,displayName:"mouse"},75:{name:"/m/0qjjc",id:75,displayName:"remote"},76:{name:"/m/01m2v",id:76,displayName:"keyboard"},77:{name:"/m/050k8",id:77,displayName:"cell phone"},78:{name:"/m/0fx9l",id:78,displayName:"microwave"},79:{name:"/m/029bxz",id:79,displayName:"oven"},80:{name:"/m/01k6s3",id:80,displayName:"toaster"},81:{name:"/m/0130jx",id:81,displayName:"sink"},82:{name:"/m/040b_t",id:82,displayName:"refrigerator"},84:{name:"/m/0bt_c3",id:84,displayName:"book"},85:{name:"/m/01x3z",id:85,displayName:"clock"},86:{name:"/m/02s195",id:86,displayName:"vase"},87:{name:"/m/01lsmm",id:87,displayName:"scissors"},88:{name:"/m/0kmg4",id:88,displayName:"teddy bear"},89:{name:"/m/03wvsk",id:89,displayName:"hair drier"},90:{name:"/m/012xff",id:90,displayName:"toothbrush"}};var d=function(){function e(e,a){this.modelPath=a||"https://storage.googleapis.com/tfjs-models/savedmodel/"+this.getPrefix(e)+"/model.json"}return e.prototype.getPrefix=function(e){return"lite_mobilenet_v2"===e?"ssd"+e:"ssd_"+e},e.prototype.load=function(){return n(this,void 0,void 0,(function(){var e,n,t;return m(this,(function(m){switch(m.label){case 0:return e=this,[4,a.loadGraphModel(this.modelPath)];case 1:return e.model=m.sent(),n=i.zeros([1,300,300,3],"int32"),[4,this.model.executeAsync(n)];case 2:return t=m.sent(),[4,Promise.all(t.map((function(e){return e.data()})))];case 3:return m.sent(),t.map((function(e){return e.dispose()})),n.dispose(),[2]}}))}))},e.prototype.infer=function(e,a,t){return n(this,void 0,void 0,(function(){var n,d,s,o,r,l,p,c,y,u,f,b;return m(this,(function(m){switch(m.label){case 0:return n=i.tidy((function(){return e instanceof i.Tensor||(e=i.browser.fromPixels(e)),i.expandDims(e)})),d=n.shape[1],s=n.shape[2],[4,this.model.executeAsync(n)];case 1:return o=m.sent(),r=o[0].dataSync(),l=o[1].dataSync(),n.dispose(),i.dispose(o),p=this.calculateMaxScores(r,o[0].shape[1],o[0].shape[2]),c=p[0],y=p[1],u=i.getBackend(),"webgl"===i.getBackend()&&i.setBackend("cpu"),f=i.tidy((function(){var e=i.tensor2d(l,[o[1].shape[1],o[1].shape[3]]);return i.image.nonMaxSuppression(e,c,a,t,t)})),b=f.dataSync(),f.dispose(),u!==i.getBackend()&&i.setBackend(u),[2,this.buildDetectedObjects(s,d,l,c,b,y)]}}))}))},e.prototype.buildDetectedObjects=function(e,a,i,n,m,d){for(var s=m.length,o=[],r=0;r<s;r++){for(var l=[],p=0;p<4;p++)l[p]=i[4*m[r]+p];var c=l[0]*a,y=l[1]*e,u=l[2]*a,f=l[3]*e;l[0]=y,l[1]=c,l[2]=f-y,l[3]=u-c,o.push({bbox:l,class:t[d[m[r]]+1].displayName,score:n[m[r]]})}return o},e.prototype.calculateMaxScores=function(e,a,i){for(var n=[],m=[],t=0;t<a;t++){for(var d=Number.MIN_VALUE,s=-1,o=0;o<i;o++)e[t*i+o]>d&&(d=e[t*i+o],s=o);n[t]=d,m[t]=s}return[n,m]},e.prototype.detect=function(e,a,i){return void 0===a&&(a=20),void 0===i&&(i=.5),n(this,void 0,void 0,(function(){return m(this,(function(n){return[2,this.infer(e,a,i)]}))}))},e.prototype.dispose=function(){null!=this.model&&this.model.dispose()},e}();e.ObjectDetection=d,e.load=function(e){return void 0===e&&(e={}),n(this,void 0,void 0,(function(){var a,n,t;return m(this,(function(m){switch(m.label){case 0:if(null==i)throw new Error("Cannot find TensorFlow.js. If you are using a <script> tag, please also include @tensorflow/tfjs on the page before using this model.");if(a=e.base||"lite_mobilenet_v2",n=e.modelUrl,-1===["mobilenet_v1","mobilenet_v2","lite_mobilenet_v2"].indexOf(a))throw new Error("ObjectDetection constructed with invalid base model "+a+". Valid names are 'mobilenet_v1', 'mobilenet_v2' and 'lite_mobilenet_v2'.");return[4,(t=new d(a,n)).load()];case 1:return m.sent(),[2,t]}}))}))},e.version="2.2.2",Object.defineProperty(e,"__esModule",{value:!0})}));
+!function(e,a){"object"==typeof exports&&"undefined"!=typeof module?a(exports,require("@tensorflow/tfjs-converter"),require("@tensorflow/tfjs-core")):"function"==typeof define&&define.amd?define(["exports","@tensorflow/tfjs-converter","@tensorflow/tfjs-core"],a):a((e=e||self).cocoSsd=e.cocoSsd||{},e.tf,e.tf)}(this,(function(e,a,i){"use strict";function n(e,a,i,n){return new(i||(i=Promise))((function(t,m){function d(e){try{o(n.next(e))}catch(e){m(e)}}function s(e){try{o(n.throw(e))}catch(e){m(e)}}function o(e){var a;e.done?t(e.value):(a=e.value,a instanceof i?a:new i((function(e){e(a)}))).then(d,s)}o((n=n.apply(e,a||[])).next())}))}function t(e,a){var i,n,t,m,d={label:0,sent:function(){if(1&t[0])throw t[1];return t[1]},trys:[],ops:[]};return m={next:s(0),throw:s(1),return:s(2)},"function"==typeof Symbol&&(m[Symbol.iterator]=function(){return this}),m;function s(m){return function(s){return function(m){if(i)throw new TypeError("Generator is already executing.");for(;d;)try{if(i=1,n&&(t=2&m[0]?n.return:m[0]?n.throw||((t=n.return)&&t.call(n),0):n.next)&&!(t=t.call(n,m[1])).done)return t;switch(n=0,t&&(m=[2&m[0],t.value]),m[0]){case 0:case 1:t=m;break;case 4:return d.label++,{value:m[1],done:!1};case 5:d.label++,n=m[1],m=[0];continue;case 7:m=d.ops.pop(),d.trys.pop();continue;default:if(!(t=d.trys,(t=t.length>0&&t[t.length-1])||6!==m[0]&&2!==m[0])){d=0;continue}if(3===m[0]&&(!t||m[1]>t[0]&&m[1]<t[3])){d.label=m[1];break}if(6===m[0]&&d.label<t[1]){d.label=t[1],t=m;break}if(t&&d.label<t[2]){d.label=t[2],d.ops.push(m);break}t[2]&&d.ops.pop(),d.trys.pop();continue}m=a.call(e,d)}catch(e){m=[6,e],n=0}finally{i=t=0}if(5&m[0])throw m[1];return{value:m[0]?m[1]:void 0,done:!0}}([m,s])}}}var m={1:{name:"/m/01g317",id:1,displayName:"person"},2:{name:"/m/0199g",id:2,displayName:"bicycle"},3:{name:"/m/0k4j",id:3,displayName:"car"},4:{name:"/m/04_sv",id:4,displayName:"motorcycle"},5:{name:"/m/05czz6l",id:5,displayName:"airplane"},6:{name:"/m/01bjv",id:6,displayName:"bus"},7:{name:"/m/07jdr",id:7,displayName:"train"},8:{name:"/m/07r04",id:8,displayName:"truck"},9:{name:"/m/019jd",id:9,displayName:"boat"},10:{name:"/m/015qff",id:10,displayName:"traffic light"},11:{name:"/m/01pns0",id:11,displayName:"fire hydrant"},13:{name:"/m/02pv19",id:13,displayName:"stop sign"},14:{name:"/m/015qbp",id:14,displayName:"parking meter"},15:{name:"/m/0cvnqh",id:15,displayName:"bench"},16:{name:"/m/015p6",id:16,displayName:"bird"},17:{name:"/m/01yrx",id:17,displayName:"cat"},18:{name:"/m/0bt9lr",id:18,displayName:"dog"},19:{name:"/m/03k3r",id:19,displayName:"horse"},20:{name:"/m/07bgp",id:20,displayName:"sheep"},21:{name:"/m/01xq0k1",id:21,displayName:"cow"},22:{name:"/m/0bwd_0j",id:22,displayName:"elephant"},23:{name:"/m/01dws",id:23,displayName:"bear"},24:{name:"/m/0898b",id:24,displayName:"zebra"},25:{name:"/m/03bk1",id:25,displayName:"giraffe"},27:{name:"/m/01940j",id:27,displayName:"backpack"},28:{name:"/m/0hnnb",id:28,displayName:"umbrella"},31:{name:"/m/080hkjn",id:31,displayName:"handbag"},32:{name:"/m/01rkbr",id:32,displayName:"tie"},33:{name:"/m/01s55n",id:33,displayName:"suitcase"},34:{name:"/m/02wmf",id:34,displayName:"frisbee"},35:{name:"/m/071p9",id:35,displayName:"skis"},36:{name:"/m/06__v",id:36,displayName:"snowboard"},37:{name:"/m/018xm",id:37,displayName:"sports ball"},38:{name:"/m/02zt3",id:38,displayName:"kite"},39:{name:"/m/03g8mr",id:39,displayName:"baseball bat"},40:{name:"/m/03grzl",id:40,displayName:"baseball glove"},41:{name:"/m/06_fw",id:41,displayName:"skateboard"},42:{name:"/m/019w40",id:42,displayName:"surfboard"},43:{name:"/m/0dv9c",id:43,displayName:"tennis racket"},44:{name:"/m/04dr76w",id:44,displayName:"bottle"},46:{name:"/m/09tvcd",id:46,displayName:"wine glass"},47:{name:"/m/08gqpm",id:47,displayName:"cup"},48:{name:"/m/0dt3t",id:48,displayName:"fork"},49:{name:"/m/04ctx",id:49,displayName:"knife"},50:{name:"/m/0cmx8",id:50,displayName:"spoon"},51:{name:"/m/04kkgm",id:51,displayName:"bowl"},52:{name:"/m/09qck",id:52,displayName:"banana"},53:{name:"/m/014j1m",id:53,displayName:"apple"},54:{name:"/m/0l515",id:54,displayName:"sandwich"},55:{name:"/m/0cyhj_",id:55,displayName:"orange"},56:{name:"/m/0hkxq",id:56,displayName:"broccoli"},57:{name:"/m/0fj52s",id:57,displayName:"carrot"},58:{name:"/m/01b9xk",id:58,displayName:"hot dog"},59:{name:"/m/0663v",id:59,displayName:"pizza"},60:{name:"/m/0jy4k",id:60,displayName:"donut"},61:{name:"/m/0fszt",id:61,displayName:"cake"},62:{name:"/m/01mzpv",id:62,displayName:"chair"},63:{name:"/m/02crq1",id:63,displayName:"couch"},64:{name:"/m/03fp41",id:64,displayName:"potted plant"},65:{name:"/m/03ssj5",id:65,displayName:"bed"},67:{name:"/m/04bcr3",id:67,displayName:"dining table"},70:{name:"/m/09g1w",id:70,displayName:"toilet"},72:{name:"/m/07c52",id:72,displayName:"tv"},73:{name:"/m/01c648",id:73,displayName:"laptop"},74:{name:"/m/020lf",id:74,displayName:"mouse"},75:{name:"/m/0qjjc",id:75,displayName:"remote"},76:{name:"/m/01m2v",id:76,displayName:"keyboard"},77:{name:"/m/050k8",id:77,displayName:"cell phone"},78:{name:"/m/0fx9l",id:78,displayName:"microwave"},79:{name:"/m/029bxz",id:79,displayName:"oven"},80:{name:"/m/01k6s3",id:80,displayName:"toaster"},81:{name:"/m/0130jx",id:81,displayName:"sink"},82:{name:"/m/040b_t",id:82,displayName:"refrigerator"},84:{name:"/m/0bt_c3",id:84,displayName:"book"},85:{name:"/m/01x3z",id:85,displayName:"clock"},86:{name:"/m/02s195",id:86,displayName:"vase"},87:{name:"/m/01lsmm",id:87,displayName:"scissors"},88:{name:"/m/0kmg4",id:88,displayName:"teddy bear"},89:{name:"/m/03wvsk",id:89,displayName:"hair drier"},90:{name:"/m/012xff",id:90,displayName:"toothbrush"}};var d=function(){function e(e,a){this.modelPath=a||"".concat("https://storage.googleapis.com/tfjs-models/savedmodel/").concat(this.getPrefix(e),"/model.json")}return e.prototype.getPrefix=function(e){return"lite_mobilenet_v2"===e?"ssd".concat(e):"ssd_".concat(e)},e.prototype.load=function(){return n(this,void 0,void 0,(function(){var e,n,m;return t(this,(function(t){switch(t.label){case 0:return e=this,[4,a.loadGraphModel(this.modelPath)];case 1:return e.model=t.sent(),n=i.zeros([1,300,300,3],"int32"),[4,this.model.executeAsync(n)];case 2:return m=t.sent(),[4,Promise.all(m.map((function(e){return e.data()})))];case 3:return t.sent(),m.map((function(e){return e.dispose()})),n.dispose(),[2]}}))}))},e.prototype.infer=function(e,a,m){return n(this,void 0,void 0,(function(){var n,d,s,o,r,l,p,c,y,u,f,b;return t(this,(function(t){switch(t.label){case 0:return n=i.tidy((function(){return e instanceof i.Tensor||(e=i.browser.fromPixels(e)),i.expandDims(e)})),d=n.shape[1],s=n.shape[2],[4,this.model.executeAsync(n)];case 1:return o=t.sent(),r=o[0].dataSync(),l=o[1].dataSync(),n.dispose(),i.dispose(o),p=this.calculateMaxScores(r,o[0].shape[1],o[0].shape[2]),c=p[0],y=p[1],u=i.getBackend(),"webgl"===i.getBackend()&&i.setBackend("cpu"),f=i.tidy((function(){var e=i.tensor2d(l,[o[1].shape[1],o[1].shape[3]]);return i.image.nonMaxSuppression(e,c,a,m,m)})),b=f.dataSync(),f.dispose(),u!==i.getBackend()&&i.setBackend(u),[2,this.buildDetectedObjects(s,d,l,c,b,y)]}}))}))},e.prototype.buildDetectedObjects=function(e,a,i,n,t,d){for(var s=t.length,o=[],r=0;r<s;r++){for(var l=[],p=0;p<4;p++)l[p]=i[4*t[r]+p];var c=l[0]*a,y=l[1]*e,u=l[2]*a,f=l[3]*e;l[0]=y,l[1]=c,l[2]=f-y,l[3]=u-c,o.push({bbox:l,class:m[d[t[r]]+1].displayName,score:n[t[r]]})}return o},e.prototype.calculateMaxScores=function(e,a,i){for(var n=[],t=[],m=0;m<a;m++){for(var d=Number.MIN_VALUE,s=-1,o=0;o<i;o++)e[m*i+o]>d&&(d=e[m*i+o],s=o);n[m]=d,t[m]=s}return[n,t]},e.prototype.detect=function(e,a,i){return void 0===a&&(a=20),void 0===i&&(i=.5),n(this,void 0,void 0,(function(){return t(this,(function(n){return[2,this.infer(e,a,i)]}))}))},e.prototype.dispose=function(){null!=this.model&&this.model.dispose()},e}();e.ObjectDetection=d,e.load=function(e){return void 0===e&&(e={}),n(this,void 0,void 0,(function(){var a,n,m;return t(this,(function(t){switch(t.label){case 0:if(null==i)throw new Error("Cannot find TensorFlow.js. If you are using a <script> tag, please also include @tensorflow/tfjs on the page before using this model.");if(a=e.base||"lite_mobilenet_v2",n=e.modelUrl,-1===["mobilenet_v1","mobilenet_v2","lite_mobilenet_v2"].indexOf(a))throw new Error("ObjectDetection constructed with invalid base model "+"".concat(a,". Valid names are 'mobilenet_v1',")+" 'mobilenet_v2' and 'lite_mobilenet_v2'.");return[4,(m=new d(a,n)).load()];case 1:return t.sent(),[2,m]}}))}))},e.version="2.2.3",Object.defineProperty(e,"__esModule",{value:!0})}));
 //# sourceMappingURL=coco-ssd.min.js.map

dist/coco-ssd.node.js

 /**
  * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
+ * Copyright 2023 Google LLC. All Rights Reserved.
  * Licensed under the Apache License, Version 2.0 (the "License");
@@ -498,3 +498,3 @@ * you may not use this file except in compliance with the License.
 // This code is auto-generated, do not modify this file!
-var version = '2.2.2';
+var version = '2.2.3';
 
@@ -534,3 +534,3 @@ /**
                         throw new Error("ObjectDetection constructed with invalid base model " +
-                            (base + ". Valid names are 'mobilenet_v1',") +
+                            "".concat(base, ". Valid names are 'mobilenet_v1',") +
                             " 'mobilenet_v2' and 'lite_mobilenet_v2'.");
@@ -550,6 +550,6 @@ }
         this.modelPath =
-            modelUrl || "" + BASE_PATH + this.getPrefix(base) + "/model.json";
+            modelUrl || "".concat(BASE_PATH).concat(this.getPrefix(base), "/model.json");
     }
     ObjectDetection.prototype.getPrefix = function (base) {
-        return base === 'lite_mobilenet_v2' ? "ssd" + base : "ssd_" + base;
+        return base === 'lite_mobilenet_v2' ? "ssd".concat(base) : "ssd_".concat(base);
     };
@@ -556,0 +556,0 @@ ObjectDetection.prototype.load = function () {

dist/index.d.ts

@@ -19,3 +19,4 @@ /**
 export { version } from './version';
-export declare type ObjectDetectionBaseModel = 'mobilenet_v1' | 'mobilenet_v2' | 'lite_mobilenet_v2';
+/** @docinline */
+export type ObjectDetectionBaseModel = 'mobilenet_v1' | 'mobilenet_v2' | 'lite_mobilenet_v2';
 export interface DetectedObject {
@@ -28,13 +29,15 @@ bbox: [number, number, number, number];
  * Coco-ssd model loading is configurable using the following config dictionary.
- *
- * `base`: ObjectDetectionBaseModel. It determines wich PoseNet architecture
- * to load. The supported architectures are: 'mobilenet_v1', 'mobilenet_v2' and
- * 'lite_mobilenet_v2'. It is default to 'lite_mobilenet_v2'.
- *
- * `modelUrl`: An optional string that specifies custom url of the model. This
- * is useful for area/countries that don't have access to the model hosted on
- * GCP.
  */
 export interface ModelConfig {
+    /**
+     * It determines wich object detection architecture to load. The supported
+     * architectures are: 'mobilenet_v1', 'mobilenet_v2' and 'lite_mobilenet_v2'.
+     * It is default to 'lite_mobilenet_v2'.
+     */
     base?: ObjectDetectionBaseModel;
+    /**
+     *
+     * An optional string that specifies custom url of the model. This is useful
+     * for area/countries that don't have access to the model hosted on GCP.
+     */
     modelUrl?: string;
@@ -41,0 +44,0 @@ }

dist/index.js

@@ -19,6 +19,7 @@ "use strict";
 var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }
     return new (P || (P = Promise))(function (resolve, reject) {
         function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
         function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
-        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }
         step((generator = generator.apply(thisArg, _arguments || [])).next());
@@ -33,3 +34,3 @@ });
         if (f) throw new TypeError("Generator is already executing.");
-        while (_) try {
+        while (g && (g = 0, op[0] && (_ = 0)), _) try {
             if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
@@ -56,2 +57,3 @@ if (y = 0, t) op = [op[0] & 2, t.value];
 Object.defineProperty(exports, "__esModule", { value: true });
+exports.ObjectDetection = exports.load = exports.version = void 0;
 var tfconv = require("@tensorflow/tfjs-converter");
@@ -62,3 +64,3 @@ var tf = require("@tensorflow/tfjs-core");
 var version_1 = require("./version");
-exports.version = version_1.version;
+Object.defineProperty(exports, "version", { enumerable: true, get: function () { return version_1.version; } });
 function load(config) {
@@ -80,3 +82,3 @@ if (config === void 0) { config = {}; }
                         throw new Error("ObjectDetection constructed with invalid base model " +
-                            (base + ". Valid names are 'mobilenet_v1',") +
+                            "".concat(base, ". Valid names are 'mobilenet_v1',") +
                             " 'mobilenet_v2' and 'lite_mobilenet_v2'.");
@@ -97,6 +99,6 @@ }
         this.modelPath =
-            modelUrl || "" + BASE_PATH + this.getPrefix(base) + "/model.json";
+            modelUrl || "".concat(BASE_PATH).concat(this.getPrefix(base), "/model.json");
     }
     ObjectDetection.prototype.getPrefix = function (base) {
-        return base === 'lite_mobilenet_v2' ? "ssd" + base : "ssd_" + base;
+        return base === 'lite_mobilenet_v2' ? "ssd".concat(base) : "ssd_".concat(base);
     };
@@ -103,0 +105,0 @@ ObjectDetection.prototype.load = function () {

dist/ssd_test.js

 "use strict";
 var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }
     return new (P || (P = Promise))(function (resolve, reject) {
         function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
         function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
-        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }
         step((generator = generator.apply(thisArg, _arguments || [])).next());
@@ -16,3 +17,3 @@ });
         if (f) throw new TypeError("Generator is already executing.");
-        while (_) try {
+        while (g && (g = 0, op[0] && (_ = 0)), _) try {
             if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
@@ -38,3 +39,2 @@ if (y = 0, t) op = [op[0] & 2, t.value];
 };
-var _this = this;
 Object.defineProperty(exports, "__esModule", { value: true });
@@ -62,3 +62,3 @@ /**
 var index_1 = require("./index");
-jasmine_util_1.describeWithFlags('ObjectDetection', jasmine_util_1.NODE_ENVS, function () {
+(0, jasmine_util_1.describeWithFlags)('ObjectDetection', jasmine_util_1.NODE_ENVS, function () {
     beforeEach(function () {
@@ -73,7 +73,7 @@ spyOn(tfconv, 'loadGraphModel').and.callFake(function () {
     });
-    it('ObjectDetection detect method should not leak', function () { return __awaiter(_this, void 0, void 0, function () {
+    it('ObjectDetection detect method should not leak', function () { return __awaiter(void 0, void 0, void 0, function () {
         var objectDetection, x, numOfTensorsBefore;
         return __generator(this, function (_a) {
             switch (_a.label) {
-                case 0: return [4 /*yield*/, index_1.load()];
+                case 0: return [4 /*yield*/, (0, index_1.load)()];
                 case 1:
@@ -91,3 +91,3 @@ objectDetection = _a.sent();
     }); });
-    it('ObjectDetection e2e should not leak', function () { return __awaiter(_this, void 0, void 0, function () {
+    it('ObjectDetection e2e should not leak', function () { return __awaiter(void 0, void 0, void 0, function () {
         var numOfTensorsBefore, objectDetection, x;
@@ -98,3 +98,3 @@ return __generator(this, function (_a) {
                     numOfTensorsBefore = tf.memory().numTensors;
-                    return [4 /*yield*/, index_1.load()];
+                    return [4 /*yield*/, (0, index_1.load)()];
                 case 1:
@@ -113,7 +113,7 @@ objectDetection = _a.sent();
     }); });
-    it('ObjectDetection detect method should generate output', function () { return __awaiter(_this, void 0, void 0, function () {
+    it('ObjectDetection detect method should generate output', function () { return __awaiter(void 0, void 0, void 0, function () {
         var objectDetection, x, data;
         return __generator(this, function (_a) {
             switch (_a.label) {
-                case 0: return [4 /*yield*/, index_1.load()];
+                case 0: return [4 /*yield*/, (0, index_1.load)()];
                 case 1:
@@ -130,6 +130,6 @@ objectDetection = _a.sent();
     }); });
-    it('should allow custom model url', function () { return __awaiter(_this, void 0, void 0, function () {
+    it('should allow custom model url', function () { return __awaiter(void 0, void 0, void 0, function () {
         return __generator(this, function (_a) {
             switch (_a.label) {
-                case 0: return [4 /*yield*/, index_1.load({ base: 'mobilenet_v1' })];
+                case 0: return [4 /*yield*/, (0, index_1.load)({ base: 'mobilenet_v1' })];
                 case 1:
@@ -144,6 +144,6 @@ _a.sent();
     }); });
-    it('should allow custom model url', function () { return __awaiter(_this, void 0, void 0, function () {
+    it('should allow custom model url', function () { return __awaiter(void 0, void 0, void 0, function () {
         return __generator(this, function (_a) {
             switch (_a.label) {
-                case 0: return [4 /*yield*/, index_1.load({ modelUrl: 'https://test.org/model.json' })];
+                case 0: return [4 /*yield*/, (0, index_1.load)({ modelUrl: 'https://test.org/model.json' })];
                 case 1:
@@ -150,0 +150,0 @@ _a.sent();

dist/version.d.ts

 /** @license See the LICENSE file. */
-declare const version = "2.2.2";
+declare const version = "2.2.3";
 export { version };

dist/version.js

 "use strict";
 /** @license See the LICENSE file. */
 Object.defineProperty(exports, "__esModule", { value: true });
+exports.version = void 0;
 // This code is auto-generated, do not modify this file!
-var version = '2.2.2';
+var version = '2.2.3';
 exports.version = version;
 //# sourceMappingURL=version.js.map

package.json

 {
   "name": "@tensorflow-models/coco-ssd",
-  "version": "2.2.2",
+  "version": "2.2.3",
   "description": "Object detection model (coco-ssd) in TensorFlow.js",
@@ -16,4 +16,4 @@ "main": "dist/coco-ssd.node.js",
   "peerDependencies": {
-    "@tensorflow/tfjs-converter": "^3.3.0",
-    "@tensorflow/tfjs-core": "^3.3.0"
+    "@tensorflow/tfjs-converter": "^4.10.0",
+    "@tensorflow/tfjs-core": "^4.10.0"
   },
@@ -24,5 +24,5 @@ "devDependencies": {
     "@rollup/plugin-typescript": "^3.0.0",
-    "@tensorflow/tfjs-backend-cpu": "^3.3.0",
-    "@tensorflow/tfjs-converter": "^3.3.0",
-    "@tensorflow/tfjs-core": "^3.3.0",
+    "@tensorflow/tfjs-backend-cpu": "^4.10.0",
+    "@tensorflow/tfjs-converter": "^4.10.0",
+    "@tensorflow/tfjs-core": "^4.10.0",
     "@types/jasmine": "~2.8.8",
@@ -35,6 +35,6 @@ "babel-core": "~6.26.0",
     "rollup-plugin-terser": "^7.0.2",
-    "rollup-plugin-visualizer": "~3.3.2",
+    "rollup-plugin-visualizer": "~5.9.2",
     "ts-node": "~8.8.2",
     "tslint": "~5.18.0",
-    "typescript": "~3.5.3",
+    "typescript": "~5.1.6",
     "yalc": "~1.0.0-pre.21"
@@ -41,0 +41,0 @@ },

README.md

@@ -70,3 +70,3 @@ # Object Detection (coco-ssd)
 
-You can also take a look at the [demo app](https://github.com/tensorflow/tfjs-models/blob/master/coco-ssd/demo).
+You can also take a look at the [demo app](https://tensorflow-js-object-detection.glitch.me/).
 
@@ -133,3 +133,3 @@ ## API
 
-This model is based on the TensorFlow object detection API. You can download the original models from [here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#coco-trained-models). We applied the following optimizations to improve the performance for browser execution:
+This model is based on the TensorFlow object detection API. You can download the original models from [here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md). We applied the following optimizations to improve the performance for browser execution:
 
@@ -136,0 +136,0 @@ 1. Removed the post process graph from the original model.

New alerts

New author

Supply chain risk

A new npm collaborator published a version of the package for the first time. New collaborators are usually benign additions to a project, but do indicate a change to the security surface area of a package.

Found 1 instance in 1 package

Fixed alerts

New author

Supply chain risk

A new npm collaborator published a version of the package for the first time. New collaborators are usually benign additions to a project, but do indicate a change to the security surface area of a package.

Found 1 instance in 1 package

Improved metrics

Total package byte prevSize

268082

increased by29.36%

Lines of code

2677

increased by0.26%

No dependency changes