Canonical: https://instain.ru/en/ai/teachable-machine/ Language: en Google Creative Lab experiment # Teachable Machine. Learning to recognize from your examples 4.43 / 5 [Directory score](https://instain.ru/en/ai/teachable-machine/#editorial-rating) [Official website ](https://teachablemachine.withgoogle.com/) Teachable Machine trains image, sound and pose recognition in a browser. ![Teachable Machine: official repository with examples and development tools.](https://instain.ru/assets/images/teachable-machine-screen-82cef73e68ca-1200.webp) Official Teachable Machine repository on GitHub. [Open the source ](https://github.com/googlecreativelab/teachablemachine-community#teachable-machine-community) ## The model learns from your examples Teachable Machine offers a visual introduction to machine learning. You collect examples, assign classes, start training and test new inputs. Official materials describe image, sound and pose recognition. The task is to distinguish the classes you prepared, rather than provide general conversation or generate illustrations. The project belongs to Google Creative Lab’s experiments. Its open repository publishes libraries, examples and an explanation of the TensorFlow.js architecture. It also warns that the experiment is not an official Google production product with guaranteed support. That limitation matters more for a learning project than the developer’s familiar name. ## Why change the background when checking Suppose you want to distinguish pencils from spoons in camera images. Collect your own examples with varied object positions, distances and lighting. Do not photograph one class only on white backgrounds and the other only on dark backgrounds: the model may learn the background rather than the objects. After training, use new images and change the capture conditions separately. Test an absent target object and an object from no known class. A high score on a familiar image says little about another room. Held-out images should represent the conditions where recognition will actually be used. ## Export and local work The official repository explains that the training and execution of models occur in the browser. Google Drive can be used separately to save the project. Exported models can be connected to sites and applications; examples for multiple languages and supporting libraries are given. This is not a promise of a ready-made cloud API for any task. When integrating a model into your own project, check uncertain predictions and new input conditions. Consequential decisions need stricter data and error controls than a learning demonstration. Open code explains the mechanism, but component licenses and the final application’s reliability still need review. Avoid collecting only near-identical neighboring frames. Show the object from different angles and test it outside its familiar setting. If the application will encounter unknown objects, define what counts as an uncertain response and what users should see. Otherwise, the classifier must choose a known class even for unsuitable input. ## Suitable tasks - Train an educational visual classifier - Export a model into your own small project Features ## Strengths - Training takes place in the browser - There are open libraries and export examples Things to consider ## Limitations - An experiment, not a production guarantee - Similar backgrounds can replace the intended class cue ## Selection and conditions Free access Yes Trial period Not confirmed No sign-up required Not confirmed Russian interface Not confirmed Local installation Yes API Not confirmed “Not confirmed” means there is not enough information. The feature may still be available; check the developer’s terms. Input formats Image, Audio, Pose Result Model, Data ### Terms Free learning experiment with open libraries. Model training is in the browser; saving the project and working your own application have separate conditions. From task to first result ## Practical example Task Train an educational visual classifier What to prepare Your own examples of two visual classes and separate images with a different background. ### Task brief or prompt Task: collect varied examples of each class, train a browser classifier and check recognition after changing the background and distance. ### Expected result An educational classification model with export options and results from testing new examples. ### How to check the result - Test new images that were not used during training. - Change the background and lighting to reveal accidental dependence on capture conditions. An editorial teaching example of a task brief, not the result of hands-on testing of the service. Check that the required mode is available, that you have rights to the source material and that the intended use of the result is permitted. Check details ## Official sources - [Official product page ](https://teachablemachine.withgoogle.com/) - [Developer Documentation and Terms ](https://github.com/googlecreativelab/teachablemachine-community) - [Developer Documentation and Terms ](https://blog.google/innovation-and-ai/products/teachable-machine/) This description is based on the developer’s material. Features, prices and availability may change; check the primary source before use. About the tool Developer Google Creative Lab Category [Code](https://instain.ru/en/category/code/) Access Google Creative Lab browser experiment; official libraries and examples are available in an open repository. Payment The educational tool and published code are free; your own application and infrastructure have separate requirements. API No separate API specified [Developer terms ](https://github.com/googlecreativelab/teachablemachine-community) Directory score 4.43 / 5 Features 4.20 / 5 Workflows 4.60 / 5 Ecosystem 4.40 / 5 Documentation 4.50 / 5 Scores are an editorial guide to the directory. They are not user reviews or the results of personal testing. [See the methodology ](https://instain.ru/en/methodology/)