October 2021
Intermediate to advanced
190 pages
4h 40m
English
Content preview from Practical Weak Supervision
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Chapter 5. Using the Snorkel-Labeled Dataset for Image Classification
The techniques described in this chapter can be used for image classification for any image datasets. This chapter will provide you with a holistic set of discussions and code that can help you get started quickly with using the dataset that has been labeled by Snorkel (from Chapter 3).
The chapter starts with a gentle introduction to different types of visual object recognition tasks and discussions on how image features are represented. Next, we discuss how transfer learning for image classification works. In the remainder of the chapter, we will use the indoor/outdoor dataset that has been labeled by Snorkel to fine-tune an image classification model using PyTorch.
Visual Object Recognition Overview
Visual object recognition is commonly used to identify objects in digital images, and consists of one or more computer vision tasks:
- Image classification
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Predict the type/class of an image (e.g., does the image consist of an indoor or outdoor scene?)
- Object localization
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Identify the objects present in an image with bounding boxes
- Object detection
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Identify the objects present in an image with bounding boxes and the type or class of the object corresponding to each bounding box
- Image instance segmentation
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Identify the objects present in an image and identify the pixels that belong to each of those objects
ImageNet is a visual database that consists of millions of images and is used by many computer vision ...
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