TF Classify is a Google tool that helps you train and deploy your own image classification models. With TF Classify, you can take advantage of transfer learning to quickly and easily train your own custom models. TF Classify makes it easy to label your images and then train and deploy your model with just a few clicks.
The latest TF Classify (Google) for PC has come up with a few updates that are better than the previous version. Want to know those? Here are they:
TF Classify (Google) Andorid App Summary
Luke Pereira has developed the TF Classify (Google) for Android. You can find it under the Entertainment category on Play Store. Since its inception, TF Classify (Google) has got over 12722+ users on board. For the user rating, it is currently holding a 3.5 rating and the latest version of it is 1.0.0
The latest update of TF Classify (Google) is Jun 20, 2017 for phones. You can’t use it on your PC directly. So, you have to use an Android emulator like BlueStacks Emulator, Memu Emulator, Nox Player Emulator, etc.
No worries, we will discuss the methods to run it on your computer using the emulators whether you use a PC or Mac. Follow the methods we’ve discussed below.
TF Classify (Google) App Overview and Details
Built from source: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android
TF Classify uses the Google Inception model to classify camera frames in real-time, displaying the top results in an overlay on the camera image.
While running TF Classify, pressing the volume keys on your device will toggle debug visualizations on/off, rendering additional info to the screen that may be useful for development purposes.
Copyright 2016 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the “License”)
Features of TF Classify (Google) for PC
-Uses the Google Inception model to classify camera frames in real-time
-Displays the top results in an overlay on the camera image
-Pressing the volume keys on your device will toggle debug visualizations on/off
-Rendering additional info to the screen that may be useful for development purposes
-Copyright 2016 The TensorFlow Authors
-Licensed under the Apache License, Version 2.0
Install “TF Classify (Google)” on PC: Windows 10/8/7 & Mac
Installing TF Classify (Google) on your PC won’t seem a big deal as it’s now, but only if you use NOX app player on your computer. It’s one of the most popular Android emulators you can use today. Let’s get you through the process of how to install TF Classify (Google) on your PC using NOX:
- First off, you need to download the NOX player on your computer from here and install it by double clicking the installer.
- The installation will take a few minutes depending on how fast your PC is. Then, click on launch and run the emulator.
- Inside the emulator, you can see the Google Play store icon on the top right corner area. Click on it.
- Play store will ask you to log into your account. Here, use your Google email and password to log in.
- Then, you can see the play store interface. On the search bar, you need to type TF Classify (Google) and hit enter to search for it.
- Once the results come up, you can see the TF Classify (Google) as the first result. Hit install under this result and it will download and install on your PC.
- After installation, you can launch the app from the shortcut button or directly from the Play store form the Open button.
|App Name:||TF Classify (Google) On Your PC|
|Devoloper Name:||Luke Pereira|
|Supporting OS:||Windows,7,8,10 & Mac (32 Bit, 64 Bit)|
|Updated on:||Jun 20, 2017|
|Get it On:|
Whats New In this TF Classify (Google)?
This application is very user-friendly and accurate. Overall, I am very pleased with its performance. It is definitely worth a try if you are in need of a good text classifier.
So, did you find the guide on how to download the TF Classify (Google) for PC above? If you did, or if you have any questions regarding it, do comment and let us know. We’re here to help.
Recent TF Classify (Google) Reviews
Cool idea but very inaccurate. If there were any way to correct the AI and help it learn, maybe with the ability to share its learning with other instances of the app (not pictures or personal content, but the identification algorithms), it would then be cooler. It identified one of the main breeds of my mixed breed dog, but identified my partner as a platypus!
Not 100% accurate, but performed very well! Even understood a slice of lemon, in a drink, in a glass, in low light. I was very impressed. Worked up close and at a distance. If anything it was more successful at more complex images like home furniture but struggled to identify a hand or a guitar.
I honestly want to know what kind of training data this guy was using. Just flat out terrible
This is great, but not in the way it means to be. Apparently, my hand is a cowboy boot and my daughter is an Italian Greyhound. But, hey, it guessed monitor or sea cucumber for my TV, so it was kinda close on that.
Not accurate when it comes to objects in my bedroom. Yet again, it could be my camera.
Recognized several items noob! Better on my phone than on my tablet. Great proof of concept.
Not bad, identified acoustic and electric guitars, a vase, a bookcase, a washing machine and a beach wagon correctly. Thought my record player was a CD player but hey, we can’t all be vinyl snobs 😉
while i can appreciate when it is right, it needs to be able to be trained better. it keeps giving me miniskirt when i point it at stuff
need a lot of training data. extremely inaccurate.
It needs lot of training.
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