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Classify the objects using deep learning techniques.


Objective:
Classify the objects using deep learning techniques.

Theory:
Image classification involves assigning a class label to an image, whereas object localization involves drawing a bounding box around one or more objects in an image. Object detection is more challenging and combines these two tasks and draws a bounding box around each object of interest in the image and assigns them a class label. Together, all of these problems are referred to as object recognition.

  • Image Classification: Predict the type or class of an object in an image.
    • Input: An image with a single object, such as a photograph.
    • Output: A class label (e.g. one or more integers that are mapped to class labels).
  • Object Localization: Locate the presence of objects in an image and indicate their location with a bounding box.
    • Input: An image with one or more objects, such as a photograph.
    • Output: One or more bounding boxes (e.g. defined by a point, width, and height).
  • Object Detection: Locate the presence of objects with a bounding box and types or classes of the located objects in an image.
    • Input: An image with one or more objects, such as a photograph.
    • Output: One or more bounding boxes (e.g. defined by a point, width, and height), and a class label for each bounding box.




Conclusion:
Object detection can be used in many areas to reduce human efforts and increase the efficiency of processes in various fields. Object detection, as well as deep learning, are areas that will be blooming in the future and making its presence across numerous fields.

There is a lot of scope in these fields and also many opportunities for improvements.

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