Welcome to the Community Computer Vision Course

Dear learner,

Welcome to the community-driven course on computer vision. Computer vision is revolutionizing our world in many ways, from unlocking phones with facial recognition to analyzing medical images for disease detection, monitoring wildlife, and creating new images. Together, we’ll dive into the fascinating world of computer vision!

Throughout this course, we’ll cover everything from the basics to the latest advancements in computer vision. It’s structured to include various foundational topics, making it friendly and accessible for everyone. We’re delighted to have you join us for this exciting journey!

On this page, you can find how to join the learners community, make a submission and get a certificate, and more details about the course!

Assignment 📄

To obtain your certification for completing the course, complete the following assignments:

  1. Training/fine-tuning a model
  2. Building an application and hosting it on Hugging Face Spaces

Training/fine-tuning a Model

There are notebooks under the Notebooks/Vision Transformers section. As of now, we have notebooks for object detection, image segmentation, and image classification. You can either train a model on a dataset that exists on 🤗 Hub or upload a dataset to a dataset repository and train a model on that.

The model repository needs to have the following:

  1. A properly filled model card, you can check out here for more information.
  2. If you trained a model with transformers and pushed it to Hub, the model card will be generated. In that case, edit the card and fill in more details.
  3. Add the dataset’s ID to the model card to link the model repository to the dataset repository.

Creating a Space

In this assignment section, you’ll be building a Gradio-based application for your computer vision model and sharing it on 🤗 Spaces. Learn more about these tasks using the following resources:

Certification 🥇

Once you’ve finished the assignments — Training/fine-tuning a Model and Creating a Space — please complete the form with your name, email, and links to your model and Space repositories to receive your certificate.

Join the community!

We invite you to be a part of our active and supportive Discord community, where engaging conversations and shared interests flourish every day and where this course started. You will find peers with whom you can exchange ideas and resources. It is your source to collaborate, get feedback, and ask questions!

It is also a good way to motivate yourself to follow the course. Joining our community is an excellent way to stay engaged. Who knows what is the next thing we will build together?

As AI continues to advance, so does the quality of our discussions and the diversity of perspectives within our community. Upon becoming a member, you’ll have an opportunity to connect with fellow course participants, exchange ideas, and collaborate with others. Moreover, the contributors to this course are active on Discord and might help you when needed. Join us now!

Computer Vision Channels

There are many channels focused on various topics on our Discord server. You will find people discussing papers, organizing events, sharing their projects and ideas, brainstorming, and so much more.

As a computer vision course learner, you may find the following set of channels particularly relevant:

If you are interested in generative AI, we also invite you to join all channels related to the Diffusion Models: #core-announcements, #discussions, #dev-discussions, and #diff-i-made-this.

What you will learn

The course is composed of theory, practical tutorials, and engaging challenges.

Throughout this course, we will cover everything from the basics to the latest advancements in computer vision. It is structured to include various foundational topics, giving you a comprehensive understanding of what makes computer vision so impactful today.

Pre-requisites

Before beginning this course, make sure that you have some experience with Python programming and are familiar with transformers, machine learning, and neural networks. If these are new to you, consider reviewing the first unit of the Hugging Face NLP course. While a strong knowledge of pre-processing techniques and mathematical operations like convolutions is beneficial, they are not prerequisites.

Course Structure

The course is organized into multiple units, covering the fundamentals and delving into an in-depth exploration of state-of-the-art models.

Meet our team

This course is made by the Hugging Face Community with love 💜! Join us by adding your contribution on GitHub. Our goal was to create a computer vision course that is beginner-friendly and that could act as a resource for others. Around 60+ people from all over the world joined forces to make this project happen. Here we give them credit:

Unit 1 - Fundamentals of Computer Vision

Unit 2 - Convolutional Neural Networks (CNNs)

Unit 3 - Vision Transformers

Unit 4 - Multimodal Models

Unit 5 - Generative Models

Unit 6 - Basic Computer Vision Tasks

Unit 7 - Video and Video Processing

Unit 8 - 3D Vision, Scene Rendering, and Reconstruction

Unit 9 - Model Optimization

Unit 10 - Synthetic Data Creation

Unit 11 - Zero Shot Computer Vision

Unit 12 - Ethics and Biases in Computer Vision

Unit 13 - Outlook and Emerging Trends

Organisation Team Merve Noyan, Adam Molnar, Johannes Kolbe

We are happy to have you here, let’s get started!

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