How can you showcase your computer vision skills on GitHub?

Selecting the right projects is crucial in demonstrating your prowess in computer vision. Through my journey, I've found that tackling projects that align with both my passion and the industry's needs creates a powerful portfolio. Whether it's developing algorithms for facial recognition or enhancing image quality, the key is to solve real-world problems with innovative AI solutions. Remember, a project that challenges you and pushes the boundaries of AI will not only be rewarding but also captivating to those who witness your work.

Choosing the right projects is crucial for showcasing your computer vision skills on GitHub. It's important to balance complexity and originality. Projects that demonstrate your ability to solve real-world problems are particularly impressive. For instance, developing an application that uses computer vision for environmental monitoring or healthcare diagnostics can be very impactful. Also, consider contributing to open-source projects, as this displays your collaborative skills and commitment to the field. Remember to thoroughly document your code and provide clear explanations of your methodologies, which is key in showing your technical proficiency and communication skills.

Following set of projects can be useful. Try adding minimum one project from each of the following classes. 1. Image classification projects. 2. Image segmentation Project. 3. Edge detection 4. Human Pose detection 5. Face detection 6. Mask Detection 7. Object detection and counting 8. Image generation using GAN 9. Image translation Many more .. In order to show your computer vision skill following path might be useful 1. Choose the tool like Tensorflow or Pytorch etc. 2. Choose the dataset you are going to work on. Use Kaggle or many data sources. 3. Try learning feature engineering in image data and identify the problem where you can implement those. 4. Model creation and Tuning 5. Model serving 6. Feedback Loop

Choosing projects on GitHub is like curating your own tech showcase. I've found success in picking ones that align with my passion – whether it's face detection or image enhancement. It's not just about complexity; it's about making it yours. My tip? Start small if needed, but make it stand out. Projects are your coding canvas; let them reflect your skills and enthusiasm.

Choose projects that challenge your current knowledge and allow you to explore novel solutions or applications. Whether it's improving on a traditional method or tackling a new dataset, ensure your projects reflect both your capabilities and potential for innovative thinking in the field. Diversify your portfolio with both simple initiatives to demonstrate foundational skills and complex systems to exhibit depth of knowledge.

Structure your repositories with a simple, yet powerful directory hierarchy. Initiate with a comprehensive introduction at the root, followed by organized folders for data, source code, and documentation. Use version control best practices by committing changes with descriptive messages and separating experimental features onto different branches.

Pour un projet IA à succès, il faut travailler avec soin et bien en amont : - sa donnée, qui doit être cohérente, hiérarchisable et reproductible ; - sa cartographie, évolutive, simplement modifiable et interconnectable.

To organize Python code, adhere to best practices such as using meaningful names following the PEP8, breaking down code into small functions and classes with clear responsibilities, structuring modules and files logically, employing version control (e.g., Git), writing unit tests, utilizing virtual environments to isolate dependencies, implementing proper error handling, documenting code comprehensively, maintaining consistent formatting with tools, and conducting code reviews for feedback. Additionally, separate concerns, use logging instead of print statements, employ a consistent file and directory structure, and keep code DRY by avoiding repetition.

One thing I found very useful is to use descriptive names everywhere in your project (directories, functions, variables). Also, Be specific about what each function do, and make sure it's in the specific directory according to what it does (whether it's a helper function, utlity class, etc..) Document, document and document, so that any person reading through your code understands it.

To showcase your computer vision skills on GitHub, structure your codebase logically. Organize it into folders representing different aspects: data preprocessing, model architectures (CNNs, RNNs), training and evaluation scripts, and maybe a separate folder for applications or demos. Include a README explaining the project, its goals, prerequisites, and instructions to run it. Utilize Jupyter Notebooks or Markdown files to detail the process, algorithms, and results. Additionally, provide a folder containing pre-trained models or weights for quick testing. Add clear comments, docstrings, and a license to enhance readability and usability for potential collaborators or employers.

One of the main problems that still persist in ML/CV domain is the reproducibility. You have to showcase your accurate results and visualize them using libraries such as matplotlib or seaborn (I prefer seaborn as its nicer compared to matplotlib). However, it is not enough just to visualize. You have to add a comprehensive readme that will help the reader reproduce the results and see the project in action through an interactive demo. I would suggest using tools like colab and wandb to do all the mentioned visualization and reproduction without any hassle.

To captivate your audience, include before-and-after comparisons, performance metrics against benchmarks, and succinct visualizations that highlight your model's effectiveness. Consider before/after image sliders or interactive elements that demonstrate the application in a simulated real-world environment.

One thing I find useful is selecting a strategic mix of metrics for presenting projects effectively, offering a comprehensive overview of health and performance. It's crucial to articulate the user experience challenges targeted by the vision project and set measurable success criteria. Highlighting a combination of quantitative metrics (precision, pixel accuracy, performance) and qualitative insights (value, end user experience) is important accompanied by a narrative around real-world applicability.

Transform your code into a compelling story by leveraging visualization tools like Matplotlib or Plotly to craft not just charts but narratives that breathe life into your data. Think beyond the traditional metrics – consider creating interactive visualizations that invite the audience to explore the nuances of your computer vision outcomes. Additionally, embrace the power of multimedia; use tools like OpenCV or Scikit-image to seamlessly integrate images and videos, offering a dynamic showcase that not only highlights your technical prowess but also communicates the real-world impact and potential applications of your computer vision projects.

The first thing we look at when reading through a research or project is result. We want to see numbers, percentages, figures, plots. This will trigger the audience attention towards going deeply into the code and understanding thr logic

Your README file should be a balance of technical detail and layman's explanation. Aim for clarity and conciseness, and ensure to guide the user through the problem statement, your approach, and the execution process. Supplement this with snapshots or GIFs of your work in action, reinforcing the narrative of your project's value.

GitHub Pages is one of the features I highly recommend. Speaking about vision, it showcases your computer vision skills clearly with many customizations, and it is an upgrade from the README file to a beautiful webpage. Leveraging GitHub Pages presents you with a website that introduces you and your computer vision skills and projects. You can use GitHub Pages to explain your background, skills, and link to your project repositories and LinkedIn profile.

Engage with the GitHub community: submit issues to projects you follow, propose fixes via pull requests, and document your development process transparently. Leverage GitHub Pages to create a professional-looking documentation or portfolio website, and make use of GitHub Actions for continuous integration to demonstrate best practices in software engineering.

Reflect on what you've learned from each project and iterate on your portfolio with these insights. Don't shy away from describing challenges faced and how you’ve overcome them. This narrative showcases your problem-solving skills and resilience – traits that are highly valued in any professional setting.

Maximize the impact of your computer vision portfolio on GitHub for interviews, Twitter promotion, and CV enhancement. - Well-documented repositories: Clear READMEs explaining projects - Visuals: Include diagrams, screenshots, demo videos - Regular Updates: Showcase ongoing learning and project enhancements - Twitter Summaries: Engaging project summaries with links - CV Highlights: Link GitHub portfolio, feature key projects - Interactive Demos: Offer live demos for firsthand experience This approach demonstrates not just technical skills but also effective communication and presentation.

Consider how your work on GitHub can be part of a larger narrative of your career. Connect your repositories to blog posts, presentations, or papers you've authored. Share anecdotes of how your computer vision projects have solved real-world problems or led to exciting opportunities. Remember, your GitHub portfolio is not just a collection of code; it's the story of your professional journey.