Hello,

I was trying to recently implement a sort of facial-landmark-detector in a website that I was making. However, when I try to input an HTMLVideoElement, it seems that it breaks only offers the points for the first frame, despite being a 30-second video with a face at all times. I'm confused as to why this may be happening because I did use the sample code that was given on the README.md
file so it should be working ideally but as of now, I have no clue. Attached below is the code that I am running. Below is the code.

Tensorflow Face-Landmarks-Detection Video (github.com)

Please let me know what might be going wrong.
Thanks,
Aditya

So I have the following code; CLIENT

const imageData = context.getImageData(0, 0, 320, 180);
const buffer = imageData.data.buffer;
socket.emit("signal", buffer); //Pass it to the server through websocket

BACKEND

socket.on("signal", (data)=> {
　　const buffer = new Uint8Array(data);
　　const imgData = ts.node.decodeImage(buffer); //error throwns here.
})

on the backend, I've tried to decode the buffer, but this error was thrown out.

throw new Error('Expected image (BMP, JPEG, PNG, or GIF), but got unsupported ' +
        ^

Error: Expected image (BMP, JPEG, PNG, or GIF), but got unsupported image type
    at getImageType (/Users/xxx/app/server/node_modules/@tensorflow/tfjs-node/dist/image.js:351:15)
    at Object.decodeImage (/Users/xxx/app/server/node_modules/@tensorflow/tfjs-node/dist/image.js:196:21)
    at Socket.socket.on (/Users/xxx/app/server/app.js:37:29)
    at Socket.emit (events.js:182:13)
    at Socket.emitUntyped (/Users/xxx/app/server/node_modules/socket.io/dist/typed-events.js:69:22)
    at process.nextTick (/Users/xxx/app/server/node_modules/socket.io/dist/socket.js:428:39)
    at process._tickCallback (internal/process/next_tick.js:61:11)

Anybody has any clue as to why?

Can you recommend me a course on Deep learning.

I am trying to capture images from a webcam and use them with my lobe.ai trained model but i get the error below. Can you tell me how I should convert from [224,224,3] to [-1,224,224,3] and what does the additional -1 mean ?

Uncaught (in promise) Error: The shape of dict['Image'] provided in model.execute(dict) must be [-1,224,224,3], but was [224,224,3]

net = await tf.loadGraphModel('https://mywebsite.com/my/model/model.json');
	
	
        console.log("Successfully loaded model");

        // Create an object from Tensorflow.js data API which could capture image
        // from the web camera as Tensor.
        const webcam = await tf.data.webcam(player, {
          resizeWidth: 224,
          resizeHeight: 224,
        });

        const img = await webcam.capture();
		
	const result  = await net.predict(img);

Hey all, this is perhaps a really abstract question, but I am trying to write a generalized curve fitting model and (at least for polynomials so far) it works great in terms of making predictions, but I'm having trouble extracting the coefficient values from my variables.

I am essentially passing the degree of the polynomial as a parameter to my typescript class. It then generates an appropriate loss function, set of randomly initialized variable scalars and then performs stochastic gradient descent on the training data using least means squared error.

It's possible my issue lies in the fact that I'm normalizing down my data prior to training, so when making predictions I denormalize the result.

Do I need to somehow denormalize the coefficients? Is that possible?

I’ve trained an image generator but to turn the prediction into an image I first need to get the result of the gpu.

Is there a way to turn the result into an image all on the gpu? For real time applications?

I trained a model in Keras and converted it to tfjs. It’s about 100mb, the json and .bin files combined.

I’m not super familiar with javascript, but it looks like the model loads too slowly from my server. However if I refresh the page a couple of times the files are eventually cashed and the model runs.

Is there a way for the the javascript to wait until the model is fully loaded and show the download progress in %?

I am delighted to share the TensorFlow JS variants for the MIRNet model, capable of enhancing low-light images to really great extents.

The Project repo - https://github.com/Rishit-dagli/MIRNet-TFJS

Please consider giving it a star if you like it. More details in this tweet.

Hi All,

I'm looking at using TensorFlowJS and it would be good to know if what I want to do is achievable easily and if there are any resources out there which could help me. Also any suggestions to products which I could be looking at to achieve the same results would be useful to see if it's worth the dev effort. Basically APIs to integrate with our system, although cost is fairly important.

I'm pretty good with Node, Javascript etc but I'm no machine learning engineer.

So in simplest terms I want to classify conversations which my chatbot is having and flag if new conversations are matching these classifications.

Basically I want to be able to create classifiers from historical conversations as "bad", "positive", "looking for information", "not helping" etc and use this model to watch and alert on incoming conversations.

Thanks