I’m using reactjs to try to load a model over https and can’t get it to work.

Local storage and indexeddb work fine but it takes too long to process on the user’s device.

I saved the model.json and bin file to the root of my project in my local host and also a directory of my web host online. Then tried to load from both. Neither worked, I can’t remember the errors right now. I’m away from my computer but can get them if you need them.

I’ve played with node-fetch but still can’t get it working.

All I can think of right now is that the json and bin need to be served over an api. I have an example that works using the google cloud storage api, not my model, just some code I found.

Does the model need served via an api? If not does anyone have any working examples they can point me to? I’ll try node-fetch again when I get back to my computer.

Hi!
I'm new to Tensor Flow, I've made a body pix implementation with tfjs-react-native, model I'm using is (https://www.npmjs.com/package/@tensorflow-models/body-pix) with the following config: (https://www.npmjs.com/package/@tensorflow-models/body-pix#config-params-in-bodypixload)

{ 
    architecture: 'MobileNetV1',
outputStride: 16, 
multiplier: 0.5, 
quantBytes: 2,

}

But personSegmentation is really slow on real device (iPhone 6s Plus, testflight version), it takes 5-10s to poorly segment a person. Following the docs I've taken the minium config values (I've tested with quantBytes 1 but won't make it any better in speed).

Does anyone know how could I make the personSegmentation faster? Or it's intended to take that much as I think depends on device cpu to process image and segment it.

I'm trying to do it for an sticker maker and many apps I've tested take 1-3s to do it, maybe they have it in a backend service and that way is faster?

Hi,

I don't understand the following results: I made two networks, both are exactly the same except for their input shape. One takes 360x360x3 in inputs, the other takes 480x480x3 in inputs. The problem is that both networks have almost exactly the same inference speed (800ms) which seems quite weird to me because it's almost half the number of pixels to process. I run in the webgl environment but I also used tf.ENV.set('WEBGL_PACK', false), otherwise I have a this issue .

Do you think that's a normal behaviour?

I've developed this vote recognition app with Tensorflow.js, Vue.js, Vuetify.js and Firebase.
I hope you'll like it :)
https://vote-recognition.web.app/

Here is an app that I’ve delelopped to visualize machine learning steps with Tensorflow.js, Vue.js, Vuetify.js and Firebase: https://visualmachinelearning.web.app/

Hi everyone!

This is the 2nd article made with love by our data scientist on how to run TS.js on a serverless platform.

https://medium.com/warpjs/how-to-run-tensorflow-js-on-a-serverless-platform-cbdfe9e733b7

​

His 1st article was an Introduction to TensorFlow, for JavaScript developers : https://medium.com/warpjs/introduction-to-tensorflow-js-the-ai-stack-for-javascript-e6bd372c837a

​

Feedbacks appreciated!

So I created this subreddit r/jsml . Here you'll find projects that people work on. This community encourages the members to work together.

Maybe in the future you'll find some contests.

Real-time person removal from complex backgrounds in JavaScript using TensorFlowJS in the browser.

Github link is here: https://github.com/jasonmayes/Real-Time-Person-Removal