In deep learning and other machine learning systems, image Annotation is the procedure of classifying or labelling an image with text, notation tools, or both so that the data attributes you wish your system to recognize on its own can be identified. Image classifiers use labelled images as input to the neural network so that the network can automatically extract and label the features of the image according to its own specifications.
The strength of the network increases with the accuracy of the classifier in classifying the images. While learning to classify images, there are pros and cons associated with it. Let’s discuss each of them so that you can make the right decision for your application.
Pros When you use an image classification application as a data Annotation tool, you can make it work as a basic machine learning model or it can be used as a standalone application. You can use the two independent ways to train different models to identify images that have to be labelled. You can also create different models depending on the kind of image that is to be labelled.
Another great benefit of using Data Annotation tool with your image processing system is that you can save time, resources and money that would have been spent on creating separate machine learning models. Image classification application is useful in many tasks such as recognizing human face, speech recognition, face recognition, document classification, object classification, video classification and many more.
Cons There are some cons of using the image classification application when you’re using the computer vision application. One of these is that it may not be able to provide high quality results especially for some applications in computer vision. Some of these applications may require high level of accuracy if you want to achieve good results. You need to keep in mind that this particular service requires high level of accuracy.
It’s important to use the correct model in order to get best results. The model plays an important role in training the computer vision models. It should be trained using the data flow and error management strategies for accuracy.
Image classifiers will be able to classify the training data scientists effectively and provide the best results when they’re trained using the Image classifiers. You should avoid using the default settings for the Image classifiers. You should train the application using the custom settings which were defined using the Dataflow or the map/algorithm tool. You can also use the batch processing setting for your Image Annotation task if you’re training data scientists to predict images from text.
The darkflow project is another great Image Annotation tool. This project was started by Chris Griffiths, who has received many accolades for his impressive work in object detection and image analysis. The darkflow project aims at providing easy access to the tools needed for web applications. Users can obtain the source code of the darkflow application via the checkout on Gitol. If you’re an expert in the field of programming, it is recommended to use the darkflow platform for all of your work.