|
| 1 | +<!DOCTYPE html> |
| 2 | +<html> |
| 3 | + <head> |
| 4 | + <meta charset="utf-8" /> |
| 5 | + <title>MobileNet Introduction</title> |
| 6 | + <!-- Import tensorflow dependencies --> |
| 7 | + <script |
| 8 | + src="https://unpkg.com/@tensorflow/tfjs" |
| 9 | + type="text/javascript" |
| 10 | + ></script> |
| 11 | + <script |
| 12 | + src="https://unpkg.com/@tensorflow-models/mobilenet" |
| 13 | + type="text/javascript" |
| 14 | + ></script> |
| 15 | + </head> |
| 16 | + <body> |
| 17 | + <h1>MobileNet with TensorFlow.js</h1> |
| 18 | + <h2 id="message">Detecting image...</h2> |
| 19 | + <img |
| 20 | + id="image" |
| 21 | + crossorigin |
| 22 | + src="https://images.unsplash.com/photo-1473496169904-658ba7c44d8a?ixlib=rb-1.2.1&auto=format&fit=crop&w=1950&q=80" |
| 23 | + /> |
| 24 | + <script> |
| 25 | + // Step 1: Load the model |
| 26 | + mobilenet.load().then((net) => { |
| 27 | + const targetImage = document.getElementById('image'); |
| 28 | + const targetText = document.getElementById('message'); |
| 29 | + |
| 30 | + // Step 2: Classify the image |
| 31 | + net.classify(targetImage).then((result) => { |
| 32 | + targetText.innerText = ` |
| 33 | + Detected: ${result[0].className} |
| 34 | + Probability: ${result[0].probability} |
| 35 | + `; |
| 36 | + }); |
| 37 | + }); |
| 38 | + </script> |
| 39 | + </body> |
| 40 | +</html> |
0 commit comments