Back to Catalog

Image classification Using hugging face for Crypto Beans

IntermediateGuided Project

The characteristics of currency are durability, portability, divisibility, uniformity, limited supply, and acceptability; all these describe beans. You are a founder of a Crypto company BeanStock that uses beans to back up crypto tokens. The token has exploded in popularity, so you need different beans for different tokens. Sorting the beans is difficult, so you fine-tune Hugging Face's pre-trained Transformers and PyTorch vision on bean dataset, getting state-of-the-art performance.

4.6 (16 Reviews)

Language

  • English

Topic

  • Computer Vision

Industries

  • Information Technology

Enrollment Count

  • 158

Skills You Will Learn

  • Python, Artificial Intelligence, Image Processing, Computer Vision, PyTorch

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 1 hour

Platform

  • SkillsNetwork

Last Update

  • May 17, 2024
About This Guided Project

About

The characteristics of currency are durability, portability, divisibility, uniformity, limited supply, and acceptability; all these describe beans. You are a founder of a Crypto company BeanStock that uses beans to back up crypto tokens. The token has exploded in popularity, so you need different beans for different tokens. Sorting the beans is difficult, so you fine-tune Hugging Face's pre-trained Transformers on your bean dataset, getting state-of-the-art performance.
You first train your Transformers to classify traffic signals then move on to the big problem of bean classification. beans_img_3leaf.png 745 KB

Why you should do this Guided Project

You can learn how to train and fit the Hugging Face Transformers model.  Here we are using the model Vision Transformer Model for Binary Classification as well as Multi-Class Image Classification. The Vision Transformer, or ViT, is a model for image classification that employs a Transformer-like architecture over patches of the image. An image is split into fixed-size patches, each of them is then linearly embedded, position embeddings are added, and the resulting sequence of vectors is fed to a standard Transformer encoder with the Help of   PyTorch vision.


A Look at the Project Ahead

Tell your audience what they can expect to learn. Better yet, tell them what they will be able to do as a result of completing your project:
  • How to use hugging Face API
  • Vision Transformer Model
  • Image Classification

What You'll Need

To complete this guided project, you will need a basic understanding of the working mechanics of Python. You will also need some prior experience working with Pre-trained Models. It will be more helpful if you have prior knowledge of the Hugging Face Transformer. 
Remember that the IBM Skills Network Labs environment comes with many things pre-installed (e.g. Docker) to save them the hassle of setting everything up. Also note that this platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari.

Instructors

Jigisha Barbhaya

Data Scientist

I am a Data scientist at IBM and Lead instructor at Skills network. I love to learn and educate. I have completed my MSc(Computer Application) specialisation in Data science from Symbiosis University.

Read more

Contributors

Svitlana Kramar

Data Scientist

I’m a passionate data science educator whose goal is to learn by teaching innovative data science tools that can improve our day-to-day tasks and our quality of life. My interests are in Natural Language Processing: text classification, summarization, and generation. Research can take a long time because there are a lot of resources and new opinions posted every day. Having tools to summarize and extract the information can save a lot of time. I hope we can all learn, approve, and apply the data science tools to cut down on the repetitive and tedious tasks, to make more informed decisions in life, to differentiate fake from real, and to open communication spaces to language-diverse or hearing-impaired audiences. The applications are limitless! My personality: I am a foodie and I love cooking and learning different cuisines. I also love travelling and connecting with people by learning a little bit of their language, about their food and music. I hold Data Science and Analytics master’s degree, specializing in Machine Learning, from University of Calgary.

Read more