ml5.js: Image Classification with MobileNet

References:

Videos:

Timestamps:
0:00 Introduction
0:26 Pretrained models
2:06 What is in the dataset?
4:00 Prediction
4:57 Supervised learning
5:46 Training dataset
6:55 ImageNet
11:09 Getting Started
12:20 Examples
14:35 Callbacks

Editing by Mathieu Blanchette
Animations by Jason Heglund
Music from Epidemic Sound

#machinelearningml #imageclassification #mobilenet #ml5js #p5js #javascript

[Samuel.Sharman]

I think this guy's energy is infectious I just wish I got this excited during class.

[juansantiagocuadra]

I have great respect for the time and energy you put into sharing so many "chucks of knowledge" in such a wide range of domains. Thank you.

[writethatdown]

The way you talk makes everything fun to learn about, it just seems like you have so much energy.

[kustomweb]

It can tell you that you are a snorkel, but it cannot tell you why. There are decisions, but no reasons. Dan Shifman is the best software teacher on YouTube.

[ralphmoran]

More than a year ago, I found your channel and videos, I'm still learning from you. Thanks much

[tahsintariq]

3:25 I can't believe Dan was actually a snorkel in disguise.

[nhasalajoshi]

the sound of Dan's kitchen bell is one of the most fun things to hear in coding tutorial videos, ever.

[PandoraMakesGames]

Wow, you're so active lately! I wish I could put out videos that fast.

[petaca]

Good video, I want to make you some queries 1- the image that I want to classify jpg must have a special size or resolution so that MobileNet processes it in an optimal or more optimized way 2- you can introduce new classifications or jpg images or another format to MobileNet for example of bacteria since I tried with some images of bacteria and it does not classify them if possible how it can be done.

[wwwdxdn]

I love your videos and i am so glad and grateful that you are teaching ML

[SrinivasMangipudi]

Dan you're so much fun! Thank you!

[davidebarranca]

In case anyone wants to use MobileNet locally (offline), it is possible to download a local copy of the model json file and reference its path, see on GitHub a repository named "ml5-data-and-models-server"

[nitscheszter]

Dan! This gave me this. Why?
0: {label: "prairie chicken, prairie grouse, prairie fowl", confidence: 0.09353400021791458}
1: {label: "quail", confidence: 0.08970478922128677}
2: {label: "drake", confidence: 0.05440836399793625}
length: 3

[gootana]

Love your videos, been following for a while and learning tons. Thanks for putting this kind of stuff out there! Do you think the different probabilities in the puffin guess was because the puffin image aspect ratio might've been changed when you resized it to the canvas?

[jonbikaku]

Could you also explain how to retrain a preexisting model using newer images ? Also, I know you love pure javascript but maybe a couple video using tensorflow and ml5 on a framework like React or Angular would be really appreciated ! Thanks!

[ioegg.]

Great tutorial!! I made a Chatbot based on Eliza and incorporated MobileNet so the bot can see.. Fun stuff, I'm adding face recognition later this week. You can see it on github and at ioegg.com
Also, maybe the reason for the different prediction confidence values for the Oystercatcher is the canvas size or image resize was different between the two tests.

[austinschaffer]

I just got to the 4:41 where you were wondering if all the probabilities need to add up to 100%, and I'm not sure they always do. For example, if the model has an entry for both "Dog" and "Bloodhound", it could be 100% sure that the current image is a dog, but only 80% sure that it's a bloodhound, or something like that.

[azulamazigh]

i like this guy. i am watching just because it's entertaining. i don't even care about js.

[enivaldobonelli]

With new versions: 1. No need for preload. 2. createIMG causes a problem, since it
takes another parameter now. loadImage works fine.

[akshitdiwan]

Sir your way of teaching is inspiring and fun!!!!