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Load Mobilenet model with Tensorflow.js and classify inside express middleware


Load Mobilenet model with Tensorflow.js and classify inside express middleware

By : user2175915
Date : October 14 2020, 02:24 PM
this one helps. You actually are assigning a top level variable to the value of an async function
code :
async function LoadModelAsync(path){
    const mn = new mobilenet.MobileNet(1, 1);
    mn.path = `file://${path}`;
    await mn.load();
    return mn;
}

var model = LoadModelAsync('CNN/model.json');
         // ^ this needs to have await infant
var model = null;
var server = null;

loadModelAsync( 'CNN/model.json' )
    .then( m => model = m )
             // ^ assigns the global model variable
    .then( () => server = app.listen(port, () => {
             // ^ assigns the global server variable and starts the server
       console.log('Started at port ' + server.address().port)
    }) );


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Exception when trying to use tensorflow classify android example on a model trained from scratch

Exception when trying to use tensorflow classify android example on a model trained from scratch


By : bgreen
Date : March 29 2020, 07:55 AM
this one helps. The problem was INPUT_SIZE. Because the default image size on inception_v3 is 299x299:
code :
  The default image size used to train this network is 299x299.
  private static final int INPUT_SIZE = 224;
Tensorflow: Importing pretrained model (mobilenet, .pb, .ckpt)

Tensorflow: Importing pretrained model (mobilenet, .pb, .ckpt)


By : MD. AL-AMIN
Date : March 29 2020, 07:55 AM
I hope this helps . tf.import_graph_def does not return the graph, it populates the "default graph" in the scope. See documentation for tf.import_graph_def for details on the return value.
In your case, you can inspect the graph using tf.get_default_graph(). For example:
code :
with gfile.FastGFile(model_filename, 'rb') as f:
  graph_def = tf.GraphDef()
  graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name='')

g = tf.get_default_graph()
print(len(g.get_operations()))
I am trying to do a quantized mobilenet model to tensorflow lite model, but run into an error

I am trying to do a quantized mobilenet model to tensorflow lite model, but run into an error


By : Zubair Ķhan
Date : March 29 2020, 07:55 AM
this one helps. I think you may be pointing to an old TensorFlow quantized mobilenet model.
We have updated quantized mobilenet models available here. The specific link for your depth multiplier of 1.0 and image size of 224 is this.
How to build tensorflow object detection model for custom classes and also include the 90 classess the SSD Mobilenet mod

How to build tensorflow object detection model for custom classes and also include the 90 classess the SSD Mobilenet mod


By : user7883959
Date : March 29 2020, 07:55 AM
Does that help The only way i find is you need to get dataset of SSD Mobilenet model on which it was trained. Make sure all the images are present in one directory and annotations in another directory. We should have a corresponding annotation file for each image file ex: myimage.jpg and myimage.xml If all the images of your customed dataset are of same formate with SSD Mobilenet model then annotate it with a tool called LabelImg. Add that images and annotated files to respective images and annotations directory where we have already saved SSD Mobilenet. Try regenerate new TFrecord and continue with remaining procedure on it.
Tensorflow SSD-Mobilenet model accuracy drop after quantization using transform_graph

Tensorflow SSD-Mobilenet model accuracy drop after quantization using transform_graph


By : Paul Curbishley
Date : March 29 2020, 07:55 AM
may help you . In this case SSD uses mobilenet as it's feature extractor . In-order to increase the speed. If you read the mobilenet paper , it's a lightweight convolutional neural nets specially using separable convolution inroder to reduce parameters .
As I understood separable convolution can loose information because of the channel wise convolution.
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