Machine learning is a technical discipline. But it is difficult to incorporate a model of machine learning than it used to be. In the Machine learning frameworks like google that eases the process of retrieving data, training model, refining future results and surfing prediction.
In Computer Vision, developing accurate machine learning models capable of finding and recognizing multiple objects in a single image remains a key challenge. The TensorFlow Object Detection API is an open-source framework simplifying the deployment and creation of training and object detection models.
The Google Brain team developed TensorFlow for internal use on Google, during 9 November 2015 under the Apache License 2.0
TensorFlow gives TensorFlow an edge over other competitors is the fact that it is open source and has huge community support, that not only provides researchers to build a new model but also a platform to interact with others that face some issues.
TensorFlow is the standard way of representing data in deep learning. Tensor is a vector or matrics of n dimension that represents datatype or operation connected in a graph and the graph is said computation that takes successively.
Many other companies also using TensorFlow.
In Tensorflow Architecture, Serviceable terms are TensorFlow Types, TensorFlow Models, Loaders, Sources, Manager and Core.
The term and its functionality in TensorFlow architecture are below.
In this paragraph will discuss Object Detection using TensorFlow. A set of images is given to the model and using the TensorFlow we trained our model.
Training of the model is done using deep learning and the main objective is to extract features and now these are visual features that are based on facial detection and many more. Features are being extracted and then created a model.
When these features are extracted and then these models are created to test the model we give test data which is again images and using this model we get final output in which object detected can be in the set of images.
And using this we get our final output in which our we have object detected in the image. The image converting in a NumPy array in a tensor flow object detection, which make easy understanding and computation.
Object detection is a computer technology related to computer vision and image processing that deals with detecting a certain type of semi-conduct object.
Detection of objects allows multiple objects with an image for recognization, detection, and localization. As a result, it gives us a much better picture of an image as a whole.
The Object Detection algorithm uses extractive features and learning algorithm models to recognize instances of an object category. Besides, it adds to applications like image retrieval, security services, and advanced driver assistance systems
TensorFlow Object plays a vital role in estimating the cost. The model detects the vehicle and saves the number plate using object detection.
When it finds the vehicle saves the number plate and records the time when it enters for parking. So when the car leaves from the parking spot it checks for the number plate and time which it has been saved when the car entered for parking and records the exit time.
By getting the entering time and exit time will get the parking time taken by the vehicle. As a result they can estimate the cost of parking.
There are mainly 3 steps in Object Detection :
To conclude, TensorFlow was a library that programmed the dataflow at its core. We’ll use the TensorFlow Python API, operating with Python 2.7 and Python 3.3 +.
Over time, TensorFlow has increased in popularity and developers now use deep learning methods for image recognition, video detection, word processing, such as sentiment analysis, and so on to solve problems.
Alternatively, using documentation, then helping the group to represent issues such as data graphs and solving them with TensorFlow would make machine learning smaller
Each graph node represents an instance of a mathematical operation, and each edge is a multi-dimensional data set each performs the operations on. In TensorFlow, all the computations are clear using data flow graphs.
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