Improve README's. (tensorflow#429)

Author: annxingyuan

blazeface/README.md

@@ -31,7 +31,7 @@ Note that this package specifies `@tensorflow/tfjs-core` and `@tensorflow/tfjs-c
 To import in npm:
 
 ```js
-import * as blazeface from '@tensorflow-models/blazeface';
+const blazeface = require('@tensorflow-models/blazeface');
 ```
 
 or as a standalone script tag:

body-pix/README-v1.md

@@ -108,7 +108,7 @@ An object containing a width, height, and a binary array with 1 for the pixels t
 ###### via NPM
 
 ```javascript
-import * as bodyPix from '@tensorflow-models/body-pix';
+const bodyPix = require('@tensorflow-models/body-pix');
 
 const outputStride = 16;
 const segmentationThreshold = 0.5;
@@ -228,7 +228,7 @@ An object containing a width, height, and an array with a part id from 0-24 for
 ###### via NPM
 
 ```javascript
-import * as bodyPix from '@tensorflow-models/body-pix';
+const bodyPix = require('@tensorflow-models/body-pix');
 
 const outputStride = 16;
 const segmentationThreshold = 0.5;

body-pix/README.md

@@ -73,7 +73,7 @@ $ npm install @tensorflow-models/body-pix
 ```
 
 ```js
-import * as bodyPix from '@tensorflow-models/body-pix';
+const bodyPix = require('@tensorflow-models/body-pix');
 
 const img = document.getElementById('image');
 

coco-ssd/README.md

@@ -48,7 +48,7 @@ There are two main ways to get this model in your JavaScript project: via script
 ```js
 // Note: you do not need to import @tensorflow/tfjs here.
 
-import * as cocoSsd from '@tensorflow-models/coco-ssd';
+const cocoSsd = require('@tensorflow-models/coco-ssd');
 
 const img = document.getElementById('img');
 

deeplab/README.md

@@ -11,8 +11,8 @@ In the first step of semantic segmentation, an image is fed through a pre-traine
 To get started, pick the model name from `pascal`, `cityscapes` and `ade20k`, and decide whether you want your model quantized to 1 or 2 bytes (set the `quantizationBytes` option to 4 if you want to disable quantization). Then, initialize the model as follows:
 
 ```typescript
-import * as tf from '@tensorflow-models/tfjs';
-import * as deeplab from '@tensorflow-models/deeplab';
+const tf = require('@tensorflow-models/tfjs');
+const deeplab = require('@tensorflow-models/deeplab');
 const loadModel = async () => {
   const modelName = 'pascal';   // set to your preferred model, either `pascal`, `cityscapes` or `ade20k`
   const quantizationBytes = 2;  // either 1, 2 or 4
@@ -34,7 +34,7 @@ By default, calling `load` initalizes the PASCAL variant of the model quantized
 If you would rather load custom weights, you can pass the URL in the config instead:
 
 ```typescript
-import * as deeplab from '@tensorflow-models/deeplab';
+const deeplab = require('@tensorflow-models/deeplab');
 const loadModel = async () => {
   const url = 'https://tfhub.dev/tensorflow/tfjs-model/deeplab/pascal/1/default/1/model.json?tfjs-format=file';
   return await deeplab.load({modelUrl: url});
@@ -49,8 +49,8 @@ You can set the `base` attribute in the argument to `pascal`, `cityscapes` or `a
 If you require more careful control over the initialization and behavior of the model (e.g. you want to use your own labelling scheme and colormap), use the `SemanticSegmentation` class, passing a pre-loaded `GraphModel` in the constructor:
 
 ```typescript
-import * as tfconv from '@tensorflow/tfjs-converter';
-import * as deeplab from '@tensorflow-models/deeplab';
+const tfconv = require('@tensorflow/tfjs-converter');
+const deeplab = require('@tensorflow-models/deeplab');
 const loadModel = async () => {
   const base = 'pascal';        // set to your preferred model, out of `pascal`,
                                 // `cityscapes` and `ade20k`

facemesh/README.md

@@ -30,7 +30,7 @@ Note that this package specifies `@tensorflow/tfjs-core` and `@tensorflow/tfjs-c
 To import in npm:
 
 ```js
-import * as facemesh from '@tensorflow-models/facemesh';
+const facemesh = require('@tensorflow-models/facemesh');
 ```
 
 or as a standalone script tag:

facemesh/demo/README.md

@@ -9,7 +9,7 @@ The MediaPipe Facemesh demo shows how to use the MediaPipe Facemesh model to est
 cd into the demos folder:
 
 ```sh
-cd facemesh/demos
+cd facemesh/demo
 ```
 
 Install dependencies and prepare the build directory:

handpose/README.md

@@ -34,7 +34,7 @@ Note that this package specifies `@tensorflow/tfjs-core` and `@tensorflow/tfjs-c
 To import in npm:
 
 ```js
-import * as handpose from '@tensorflow-models/handpose';
+const handpose = require('@tensorflow-models/handpose');
 ```
 
 or as a standalone script tag:

handpose/demo/README.md

@@ -9,7 +9,7 @@ This demo shows how to use the handpose model to detect hands in a video stream.
 cd into the demos folder:
 
 ```sh
-cd handpose/demos
+cd handpose/demo
 ```
 
 Install dependencies and prepare the build directory:

knn-classifier/README.md

@@ -67,9 +67,9 @@ You can see example code [here](https://github.com/tensorflow/tfjs-models/tree/m
 ###### via NPM
 
 ```js
-import * as tf from '@tensorflow/tfjs';
-import * as mobilenetModule from '@tensorflow-models/mobilenet';
-import * as knnClassifier from '@tensorflow-models/knn-classifier';
+const tf = require('@tensorflow/tfjs');
+const mobilenetModule = require('@tensorflow-models/mobilenet');
+const knnClassifier = require('@tensorflow-models/knn-classifier');
 
 // Create the classifier.
 const classifier = knnClassifier.create();

mobilenet/README.md

@@ -50,7 +50,7 @@ There are two main ways to get this model in your JavaScript project: via script
 ```js
 // Note: you do not need to import @tensorflow/tfjs here.
 
-import * as mobilenet from '@tensorflow-models/mobilenet';
+const mobilenet = require('@tensorflow-models/mobilenet');
 
 const img = document.getElementById('img');
 

posenet/README.md

@@ -152,7 +152,7 @@ It returns a `Promise` that resolves with a  **single** `pose`. The `pose` has a
 ###### via NPM
 
 ```javascript
-import * as posenet from '@tensorflow-models/posenet';
+const posenet = require('@tensorflow-models/posenet');
 
 async function estimatePoseOnImage(imageElement) {
   // load the posenet model from a checkpoint
@@ -406,7 +406,7 @@ It returns a `promise` that resolves with an array of `pose`s, each with a confi
 ###### via NPM
 
 ```javascript
-import * as posenet from '@tensorflow-models/posenet';
+const posenet = require('@tensorflow-models/posenet');
 
 async function estimateMultiplePosesOnImage(imageElement) {
   const net = await posenet.load();

qna/README.md

@@ -47,7 +47,7 @@ There are two main ways to get this model in your JavaScript project: via script
 ```js
 // Note: you do not need to import @tensorflow/tfjs here, but make sure you have installed the peer dependencies for tfjs-core and tfjs-converter.
 
-import * as qna from '@tensorflow-models/qna';
+const qna = require('@tensorflow-models/qna');
 
 // Load the model.
 const model = await qna.load();

speech-commands/README.md

@@ -42,8 +42,8 @@ To use the speech-command recognizer, first create a recognizer instance,
 then start the streaming recognition by calling its `listen()` method.
 
 ```js
-import * as tf from '@tensorflow/tfjs';
-import * as speechCommands from '@tensorflow-models/speech-commands';
+const tf = require('@tensorflow/tfjs');
+const speechCommands = require('@tensorflow-models/speech-commands');
 
 // When calling `create()`, you must provide the type of the audio input.
 // The two available options are `BROWSER_FFT` and `SOFT_FFT`.
@@ -132,8 +132,8 @@ shape of the Tensor must match the expectation of the recognizer instance.
 E.g.,
 
 ```js
-import * as tf from '@tensorflow/tfjs';
-import * as speechCommands from '@tensorflow-models/speech-commands';
+const tf = require('@tensorflow/tfjs');
+const speechCommands = require('@tensorflow-models/speech-commands');
 
 const recognizer = speechCommands.create('BROWSER_FFT');
 

toxicity/README.md

@@ -23,7 +23,7 @@ Using `npm`:
 To import in npm:
 
 ```js
-import * as toxicity from '@tensorflow-models/toxicity';
+const toxicity = require('@tensorflow-models/toxicity');
 ```
 
 or as a standalone script tag:

universal-sentence-encoder/README.md

@@ -33,7 +33,7 @@ Using `npm`:
 To import in npm:
 
 ```js
-import * as use from '@tensorflow-models/universal-sentence-encoder';
+const use = require('@tensorflow-models/universal-sentence-encoder');
 ```
 
 or as a standalone script tag: