Image recognition is one of the features of TensorFlow, so let us check how good it is? Also, let’s have a small insight into how to do it.
TensorFlow Image Recognition Introduction
The purpose of TensorFlow is for building a neural network model.
These models should automatically recognize an image.
Also, it first releases were by Google in the year 2015. Then boom, it becomes one of the most popular machine learning libraries.
Here are the two approaches in TensorFlow image recognition,
- Classifying the Convolutional neural network in recognizing objects. Such as animals, humans, scenery, etc.
- Object Detection Artificial Programming Intelligence (API). Detecting an object that is more powerful than classifying objects. Because you can do multiple detecting in one image.
Also, other facts about the TensorFlow object detection API. It is built for helping build, train, and deploy object detection models.
Also, the API uses the Residual Neural Network – 50 and -101 features for object detection.
And the most amazing part is the features have extractors trained on Dataset for over 4 million iterations.
The Good Thing About TensorFlow
How good is TensorFlow image recognition? Aside, from its usage and purpose.
Tensorflow is the most active and simplest way to do image recognition. It can be done on a laptop or computer without the Graphic Processing unit because of the API.
In processing, some steps need to make.
- The model from the TensorFlow repository should be downloaded. After you download it to your computer, extract it from the root folder. If you are using Windows you can extract it in the “C” drive and name it “models”
- The second is the command line. You should open the command prompt as admin. Then, run the “classify.image.py” file. This file is in “models -> tutorials -> imagenet -> classify_image.py” the after this you click enter.
- Downloading the image then copy and paste it to “models -> tutorials -> imagenet -> classify_image.png” directory.
- Using command prompt to perform recognition by just editing the “-image_file”.
How Good is TensorFlow At Image Recognition?
Here is the list of how TensorFlow becomes a good thing for image recognition.
- When it comes to graphing, TensorFlow has the best computational graph visualizations. Compared to other libraries such as Theano and Torch.
- Library management also the best. Thanks to Google that backing up. Also, its advantages are the onpoint performance, the immediate updates, and frequent new releases of new features.
- The debugging that helps for executing subpart of graphs. Also, it gives the upper hand for retrieving discreet data.
- Has a big capacity. The libraries are put on the hardware. This is also, the cellular device to the computer.
- It is created to use different backend software. Such as ASIC, GPUs, etc also it is highly parallel.
- Because of its back up, TensorFlow has outstanding community maintenance.
- When it comes to the training approach it has a unique process. From models down to the metrics.
- Lastly, its performance is high and one of the best in the industry. 4
