# Labellerr Notebooks: computer vision tutorials organised by industry, not by model > The Labellerr Hands-On Learning in Computer Vision repository is a growing notebook collection covering object detection, segmentation, pose estimation and OCR, filed under industry folders rather than model folders. It is a good entry point for a practitioner who wants a working example, and a poor fit for anyone who needs a maintained library. **Labellerr/Hands-On-Learning-in-Computer-Vision** — Hands-On Learning in Computer Vision - Repository: https://github.com/Labellerr/Hands-On-Learning-in-Computer-Vision - Stars: 597 · Forks: 118 - Language: Jupyter Notebook - License: MIT - Published: 2026-10-22 · Updated: 2026-10-22 · Language: en - Canonical page: https://hysenlabs.com/projects/labellerr-hands-on-learning-in-computer-vision ## The repository is organised by industry, not by architecture The top level of the repository is a set of industry folders: Automobile, Construction, Healthcare, Manufacturing, Retail, Robotics, Sports, Life Sciences and Biotechnology, Security, plus directories named Experiments, SDK Tutorials, Model Notebooks, Egocentric_Vision_Usecase, Build your AI Agents and fine-tune YOLO for various use cases. That structure is the design decision worth understanding. Most public material on computer vision is filed by model, so a folder of YOLO notebooks and a folder of segmentation notebooks, and a practitioner has to translate their own problem into a model category before finding anything. Here, the entry point is the domain. Someone with a factory line looks in Manufacturing, someone counting items on a belt looks in the fine-tune YOLO folder, and the model is whatever the notebook happens to use. The trade-off is real. A domain-first layout makes discovery easy and makes coverage uneven, because there is no guarantee that two notebooks in different folders use the same model, the same dataset, or even the same library version. Consistency across the repository is not something the folder structure gives you. ## The examples are use cases, not model benchmarks The model notebook table in the README lists concrete tasks rather than abstract model names. An AI Smart Waste Classifier for a waste sorting line. AI Electronics Detection doing instance segmentation with YOLO 11 on electronic chips. An AI Mask Detection System. An AI Conveyor Belt Counter for real-time dual-lane monitoring. A Smart City Infrastructure Analysis using AI and drone segmentation on city land use imagery. And a fine-tune of YOLOv8 Pose for cricket bowling analysis. The last one is the clearest signal of the intent. A pose model is not being benchmarked on a dataset metric. It is being pointed at cricket footage, which means the notebook is a worked example of a task, a dataset somebody assembled, and a set of choices about what counts as a delivery. The README also describes the collection more broadly as covering AI Agents, computer vision, LLMs and AI, with vision tasks ranging from object detection and segmentation through object tracking and OCR. The stated goal is a growing collection of the latest tutorials, and it is a teaching repository rather than a benchmark suite. ## Colab badges are the intended way to run most of these Every notebook in the table carries a Jupyter link and a Colab link, and the Colab entry opens the same file through Google Colab. The primary language recorded for the repository is Jupyter Notebook, and there is no package manifest, no library entry point and no install command anywhere in the documentation. So there is no installation step to describe. The practical path is to open a notebook in Colab from the repository, or clone the repository and run it in a local Jupyter environment where the needed libraries are already present. That absence is not an oversight, it is the shape of the project. There is no requirements file to keep in sync, no version to pin, and no release to install. The trade is that a notebook which ran when it was written may not run today, because the training code it calls may have changed underneath it. This is the normal risk profile of tutorial code, and it is worth checking before you base a pipeline on any single cell of it. ## Fine-tuning is the recurring method across the notebooks The folder name fine-tune YOLO for various use cases tells you the method before you open a single file. Most of the collection is about taking an existing detector and adapting it to a specific domain, rather than implementing a detection algorithm from scratch. The examples reflect that pattern. Electronic chips get an instance segmentation model built on YOLO 11, city land use imagery gets a segmentation treatment suited to drone footage, waste sorting gets a classifier, and cricket bowling gets pose estimation rather than plain detection. Each of these is a decision about output type, and the choice follows the physical problem: counting objects on a moving belt is a detection and counting problem, cricket delivery analysis is about a body, segmentation is for anything where the shape matters rather than the box. What the repository does not present is a comparison layer. There is no table of accuracy figures across models, no statement of which approach suits which dataset size, and no guidance on how much data you need before fine-tuning beats prompting or a prebuilt API. The notebooks show a path, not the trade space. ## What the collection does not give you It is worth being direct about the limits, because a repository like this invites the assumption that it is a library. There is no importable package. The MIT licence covers the repository contents, but nothing here is published to PyPI or npm, so you cannot add it to a requirements file. There is no API, no config schema, and no interface other than a notebook cell. There are no releases either: the repository publishes no GitHub releases, so there is no version history to cite and no changelog to read. The other limit is coverage. The README calls the collection growing, and the industry folders are unevenly filled, with several domains represented by a folder name and a handful of notebooks while others hold more. A contributor guide and a SECURITY.md are present, which tells you the project accepts outside contributions, but neither is a substitute for an owner who maintains examples against every upstream change. If your requirement is a component you deploy, this repository is the wrong shape entirely. If your requirement is to understand a task by reading a worked notebook, it is exactly the right shape. ## Where this sits against writing your own pipeline The realistic alternative to a notebook collection is to build the training script yourself, using the upstream implementation of the model directly. The difference in approach is concrete. A notebook arrives with the decisions already made and the plumbing visible. You see the dataset directory layout, the annotation format, the config file, the training call and the evaluation step in one scroll, which is exactly the information a blog post omits and a bare model repository does not show. A self written script starts from a blank file, so you spend the first day deciding what a config for your dataset has to contain. The other difference is cost. A notebook is a snapshot that stops matching upstream code at some unknown point, and repairing it is work you do alone. A script you write against the current upstream version is yours to keep, and it is easier to update because you know which pieces you added. So the notebook is the cheaper path to a first working model, and the script is the better path to something you still maintain next year. The practical way to use this collection is to read a notebook, then rebuild the core of it in your own repository with pinned dependency versions, rather than to import from it. ## Conclusion Adopt this repository if you want a runnable notebook for a specific computer vision task, such as waste classification, mask detection, conveyor counting or cricket bowling analysis, and you are happy to work from Colab or a local Jupyter environment. Do not adopt it as a dependency, since a notebook folder is not an installable package, and do not expect the examples to be maintained alongside the models they use. Before you build on any of it, open the notebook you intend to use and check which model version and dataset it pins, since the repository publishes no releases and the last push was on 2026-10-15, and read the LICENSE file and CONTRIBUTING.md before reusing the code in your own project. ## FAQ ### How do I run the Labellerr computer vision notebooks? Each notebook in the README table links to both a Jupyter file in the repository and a Colab version, so you can open it directly in Google Colab or clone the repository and run it in a local Jupyter environment. The repository has no install command or package manifest. ### Which computer vision models do the Labellerr notebooks use? The listed examples include a waste classifier, instance segmentation of electronic chips with YOLO 11, mask detection, a conveyor belt counter, city land use segmentation from drone imagery, and a fine-tune of YOLOv8 Pose for cricket bowling analysis. The folder layout centres on fine-tuning YOLO for various use cases. ### Is the Labellerr Hands-On Learning in Computer Vision repository a library I can install? No. The project is a collection of Jupyter notebooks with no package manifest, no API and no releases, so it is meant to be read and run rather than depended on. The licence is MIT. ### How is the Labellerr repository organised? The top level is made up of industry folders such as Automobile, Construction, Healthcare, Manufacturing, Retail, Robotics, Sports, Life Sciences and Biotechnology and Security, alongside Model Notebooks, SDK Tutorials, Experiments, Egocentric_Vision_Usecase, Build your AI Agents and fine-tune YOLO for various use cases. ## Sources - [Issues](https://github.com/Labellerr/Hands-On-Learning-in-Computer-Vision/issues) - [Labellerr/Hands-On-Learning-in-Computer-Vision on GitHub](https://github.com/Labellerr/Hands-On-Learning-in-Computer-Vision) - [License: MIT](https://github.com/Labellerr/Hands-On-Learning-in-Computer-Vision/blob/main/LICENSE) - [README](https://github.com/Labellerr/Hands-On-Learning-in-Computer-Vision/blob/main/README.md) --- Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/labellerr-hands-on-learning-in-computer-vision