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Note: This project is just a basic intro for begineers who are just starting out into deep learning just like me. I'm not an expert or anything and my code and explanations may or may not be true 100% of the time. With that said, lets get started shall we.
- Difference between Image Classification, Object Detection and Image Segmentation
- Image Classification
In simple words, image classification is a technique that is used to classify or predict the class of a specific object in an image. The main goal of this technique is to accurately identify the features in an image. Convolutional Neural Networks (CNNs) is the most popular neural network model that is used for image classification problem.
- Object Detection
It helps us to identify the location of a particular object/objects in the given image. In case we have multiple objects present, we then rely on the concept of Object Detection. We can predict the location along with the class for each object using Object Detection. Object detection techniques can be used in real-world projects such as face detection, pedestrian detection, vehicle detection, traffic sign detection, video surveillance, among others. The popular models include MobileNet, You Only Live Once (YOLO), Mark-RCNN, RetinaNet, among others.
- Image Segmentation
It’s not a great idea to process the entire image at the same time as there will be regions in the image which do not contain any information. By dividing the image into segments, we can make use of the important segments for processing the image. That, in a nutshell, is how Image Segmentation works.
You can refer the following links for better understanding
References: Link 1 | Link 2 | Video
Convolution