examples
awesome-teachable-machine
examples | awesome-teachable-machine | |
---|---|---|
7 | 90 | |
390 | 183 | |
2.6% | 3.8% | |
6.8 | 0.0 | |
4 months ago | almost 2 years ago | |
Jupyter Notebook | ||
Apache License 2.0 | Creative Commons Zero v1.0 Universal |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
examples
- FLaNK-AIM Weekly 06 May 2024
- FLaNK Stack Weekly for 07August2023
-
Vector database built for scalable similarity search
As another commenter noted, Milvus is overkill and a "bit much" if you're learning/playing.
A good intro to the field with progression towards a full Milvus implementation could be starting with towhee[0] (which is also supported by Milvus).
towhee has an example to do exactly what you want with CLIP[1].
[0] - https://towhee.io/
[1] - https://github.com/towhee-io/examples/tree/main/image/text_i...
-
Ask HN: Any good self-hosted image recognition software?
Usually this is done in three steps. The first step is using a neural network to create a bounding box around the object, then generating vector embeddings of the object, and then using similarity search on vector embeddings.
The first step is accomplished by training a detection model to generate the bounding box around your object, this can usually be done by finetuning an already trained detection model. For this step the data you would need is all the images of the object you have with a bounding box created around it, the version of the object doesnt matter here.
The second step involves using a generalized image classification model thats been pretrained on generalized data (VGG, etc.) and a vector search engine/vector database. You would start by using the image classification model to generate vector embeddings (https://frankzliu.com/blog/understanding-neural-network-embe...) of all the different versions of the object. The more ground truth images you have, the better, but it doesn't require the same amount as training a classifier model. Once you have your versions of the object as embeddings, you would store them in a vector database (for example Milvus: https://github.com/milvus-io/milvus).
Now whenever you want to detect the object in an image you can run the image through the detection model to find the object in the image, then run the sliced out image of the object through the vector embedding model. With this vector embedding you can then perform a search in the vector database, and the closest results will most likely be the version of the object.
Hopefully this helps with the general rundown of how it would look like. Here is an example using Milvus and Towhee https://github.com/towhee-io/examples/tree/3a2207d67b10a246f....
Disclaimer: I am a part of those two open source projects.
-
Deep Dive into Real-World Image Search Engine with Python
I have shown how to Build an Image Search Engine in Minutes in the previous tutorial. Here is another one for how to optimize the algorithm, feed it with large-scale image datasets, and deploy it as a micro-service.
-
Build an Image Search Engine in Minutes
The full tutorial is at https://github.com/towhee-io/examples/blob/main/image/reverse_image_search/build_image_search_engine.ipynb
awesome-teachable-machine
-
Ask HN: Tool(s) to calculate horse hoof angles
Not sure if I've seen anything of the sort, seems rather specific. Maybe try a Teachable Machine project? https://teachablemachine.withgoogle.com/
- Google Teachable Machine
-
What is Machine Learning?
Train a computer to recognize your images, sounds, and poses. Use this resource to gain a better understanding.
- Unleashing the Power of TensorFlow: Integrating Machine Learning Magic into Your Flutter Apps 🚀✨
-
Is there any neural network or LLM like chatgpt,midjourney that can help us train and generate custom sounds
[Teachable Machine](https://teachablemachine.withgoogle.com/)
-
Building Simple and Customizable Image Classifier with Teachable Machine and Python
We will create an machine learning model that can classify Arabic and English books. To collect, train, and test data, we will use Teachable Machine from Google.
-
SOOO...where should i learn machine learning for free??
a lot of places! but for a high schooler, better to focus at what you want to do first. or if you still haven't gotten any idea, try a simple explanation on what ml is without the math on youtube and tinker around a no code machine learning platform like https://teachablemachine.withgoogle.com/
-
Which tools to use for my project?
The principle is roughly the same as it is possible with teachablemachine.withgoogle.com.
-
Is AI or ML something I can learn on the side for side projects and fun/hobby, or is it something that needs to be taken “serious” and need a college degree to actually learn it?
You mentioned app, so check this out: https://teachablemachine.withgoogle.com
-
I made a React Native Web app that uses ML to label image data in the browser
Here is a similar tool and source
What are some alternatives?
towhee - Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.
teachablemachine-node - Using Teachable Machine Models in Node.js
milvus-lite - A lightweight version of Milvus
examples - TensorFlow examples
gorilla-cli - LLMs for your CLI
android-bootstrap - Bootstrap your Lobe machine learning model with our Android project.
anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
public-apis - A collective list of free APIs
EverythingApacheNiFi - EverythingApacheNiFi
FtcRobotController
OpenBuddy - Open Multilingual Chatbot for Everyone
teachablemachine-community - Example code snippets and machine learning code for Teachable Machine