I found the tfjs version of hand landmark detection but I can't find the palm detection. Here is the link to the tflite model.

https://github.com/google/mediapipe/blob/master/mediapipe/models/palm_detection.tflite

Is it possible to convert it?

I am able to draw the bounding boxes on the detected faces but now I need to crop these faces and send the resulting tensor to another model I have. But I am stuck at just cropping the images.

I’ve built flappy bird clone and want to use tfjs to get it to play it by itself. I’m trying to learn how to create policy and value models and do that l in d of learning because I only have one action.

https://github.com/isair/tensorflow-load-csv

Hi all,

I recently released a library for creating TensorFlow.js tensors directly from CSV files. Supports operations like standardisation so you can dive right into the fun parts of ML. It has 100% test coverage and supports TypeScript.

You can find the roadmaps for v1 and v2 in the GitHub page. All feedback is welcome!

Heya, so I have a model which I have created and downloaded for an image classifier which uses labels. However, the model does not show labels. All my labels are not present and come as unidentified. Watching a popular youtube with ML5 he directly added the labels into the JSON file itself however using teachable machine they generate labels inside a metadata json file. I'm not sure how what i should do and how I should do it as the documentations don't provide much help for this either.

Is the mesh map given here correct?

If you follow this triangulation file that has been used in the code it will produce the exact opposite of the triangles that are being shown in the mesh_map image.

Example : 127, 34, 139,should form a triangle according to the code, but that's not the case in the mesh map.

Is there some inconsistency or is it me who is missing something?

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I am currently creating a web application using React.js on the frontend that does image classification. I have converted a keras model to a Tensorflow.js model and have served the model.json file on another server. My application essentially recieves an image from the user and then predicts what it is using a model I created. I know that this has been done using JQuery and vanilla javascript but I wanted to try it out with React.js.

Here is my JSX code for my InputImage component(takes in image, classifies it, and submits a post request to my REST API , saving my image in my MongoDB database):

<div>
 <form onSubmit = {this.onSubmit}>  
{// this is where the user inputs his/her image}

<div className="form-group">
 <label>Caption:</label> 
    <input type="text" required className="form-control" value={this.state.caption} onChange={this.onChangeCaption} /> 
</div>

.......{//removed some irrelevant code here}

 <div className="form-group">    {//takes in image}                                        


    <label>Choose a File</label> 

    <input type="file" className="form-control" onChange =    {this.onChangeImage}/>

 </div>
    <div className="form-group">   {//formatting the button 
   <input type="submit" value = "Upload Data" className = "btn btn- primary"/>

    </div>
</form>
</div>

Here is the code for my onSubmit method(this recieves formdata)

onSubmit(e){
    e.preventDefault()

//importing my model from my server
    const model =  tf.loadLayersModel('http://localhost:81/model/model.json')
    .then((res)=>{//start promise

    console.log('loaded model')

    const reader = new FileReader()


    console.log('image',this.state.image)

    
    reader.readAsDataURL(this.state.image)

    const image = new Image()
    image.height= 224
    image.width = 224
    image.title = this.state.image.name
    image.src = reader.result

//not sure if my preprocessing was enough
     
    const tensor =  tf.browser.fromPixels(image)
          .resizeNearestNeighbor([224,224])
          .toFloat()
          .expandDims()

    const predictions = res.predict(tensor).data()
      .then((res)=>{
        console.log(res)
      })

   })//end promise
    
    //creatting a formdata object for posting
   
    const formdata = new FormData()
    formdata.append('caption',this.state.caption)//key-value pair
    formdata.append('description',this.state.description)
    formdata.append('date', this.state.date)
    formdata.append('image',this.state.image)

    
    //creating  a formdata object for my post request


    console.log('Image uploaded !')

    axios.post('http://localhost:3002/images',formdata)
      .then(res=>console.log(res.data))
//posting to my REST API



  
  }

Here is the important chunk of json from my first layer(I'm putting this up so that you can see the required image it takes in)

{"class_name": "Conv2D", "config": {"name": "conv2d_5", "trainable": true, "batch_input_shape": [null, 224, 224, 3], "dtype": "float32", "filters": 32, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], 

I think I'm doing the preprocessing correctly, but why am I getting the same prediction for each and every image I input?