Train a Deep Learning model for custom image classification using Teachable Machine

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This video shows step by step tutorial on how to train an image classification model for a custom object dataset using Teachable Machine. The custom image classification model trained here is with & without a face mask.

① ⚡⚡ My Website Blog post on this ⚡⚡

② ⚡⚡ My Medium post on this⚡⚡

③ ⚡⚡ Teachable Machine site image project train link ⚡⚡

④ ⚡⚡ Dataset used for this ⚡⚡

#teachablemachine #imageclassification #customimageclassification #mobilenet #maskdetection #SSDModelTraining #facemaskdetection #deeplearning #machinelearning

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Making these videos takes a lot of time and effort, so if you like these videos and if you can, then please support the channel using any of the following:

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Thanks for watching!

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Great Video! thanks for your help!
Can you make a video on training a custom mask rcnn model on google colab?

waleedak

bro im using a model created with teachable machine in react native but its really slow in giving predictions it takes 200ms to 500 ms depending on the internet speed . Because of this it makes the camera very laggy my code is below
im usine Tensorflow js in react native with below code :-
<TensorCamera
style={styles.camera}

resizeWidth={224}
resizeHeight={224}
resizeDepth={3}
onReady={handleCameraStream}
autorender={true}
/>
//
const handleCameraStream = (imageAsTensors) => {
try {
} catch (e) {
// console.log("Tensor 1 not found!");
}
const loop = async () => {
// && detected == true
if (model !== null) {
if (frameCount % makePredictionsEveryNFrames === 0) {
const imageTensor = imageAsTensors.next().value;
await getPrediction(imageTensor);
// .catch(e => console.log(e));
}
}

frameCount += 1;
frameCount = frameCount % makePredictionsEveryNFrames;
requestAnimationFrameId = requestAnimationFrame(loop);
};
loop();

//loop infinitely to constantly make predictions
};
//
const getPrediction = async (tensor) => {
// if (!videoLink) {
if (!tensor) {
console.log("Tensor not found!");
return;
}
//
const imageData2 = tensor.resizeBilinear([224, 224]);
// tf.image.resizeBilinear(tensor, [224, 224]);
const normalized =
const final = tf.expandDims(normalized, 0);
//
console.time();
const prediction =

console.timeEnd();
console.log("Predictions:", prediction);
}

gigachad

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