Gain insights into Hugging Face’s AI models for NLP and computer vision. Explore transformer-based pipelines, apply them for tasks like classification and object detection, using Python and PyTorch.

Applying Hugging Face Machine Learning Pipelines in Python.png

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JuPyter

JuPy-Translation

JuPy-QA

JuPy-Text-Classification

JuPy-Summarization

JuPy-Text-Generation

JuPy-Similarity

JuPy-Fill-Mask

JuPy-Image-Classification

JuPy-Object-Detection

JuPy-Segmentation

JuPy-Image-to-Text

Hugging Face is a community-driven effort to develop and promote artificial intelligence for a wide array of applications. The organization’s pre-trained, state-of-the-art deep learning models can be deployed to various machine learning tasks.

In this course, you’ll explore the Hugging Face artificial intelligence library with particular attention to natural language processing (NLP) and computer vision. You’ll first explore Hugging Face’s approach to deep learning with specific attention to transformers. You’ll then learn Hugging Face’s pipeline API model and apply various pipelines to unique NLP tasks such as classification, summarization, question answering, and more. You’ll continue with a new set of Hugging Face pipelines for computer vision tasks including object detection and segmentation.

By the end of this course, you’ll be familiar with a wide array of Hugging Face’s pipelines for common machine learning tasks and their implementation in Python using pytorch.

Applying Hugging Face Machine Learning Pipelines in Python

Segmentation has many applications, such as medical imaging, autonomous cars, or image retrieval.  While it may seem like a straightforward task, it requires some appropriate learning before partitioning the pixels.

# Overview

Image segmentation is used to partition the image into different segments (sets of pixels) corresponding to a distinct object. 

It is usually used for detailed information about the image segments rather than object detection. It finds its use in several applications, especially in biomedical imaging. It can be divided into the following three types:

- **Semantic**: This refers to pixel-level segmentation 
- **Instance**: This is similar to semantic segmentation and is also done on the pixel-level. Here, however, each instance is treated as a distinct segment. For example, instance segmentation of an image of three cats results in all three cats being treated as separate instances. 
- **Panoptic**: This is a new type of segmentation and is a hybrid of both semantic and instance segmentation.

# Inference

Hugging Face provides us with models for all of three aforementioned types. To perform any of the three segmentations, we can simply call the respective model.

To use the pipeline for segmentation, we need to install the PyTorch image models library:

 



Learn to perform segmentation using Hugging Face API.

Segmentation

Introduction

NLP

Computer Vision

Conclusion

Appendix

Segmentation

Overview