habitat-sim
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habitat-sim | bootcamp | |
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5 | 24 | |
2,351 | 1,606 | |
3.0% | 2.9% | |
8.8 | 9.1 | |
5 days ago | 8 days ago | |
C++ | HTML | |
MIT License | Apache License 2.0 |
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habitat-sim
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Break into CV with background in biological vision and neuroscience
Spot on! I think you grasped the general idea. During some of my 3d studies, I collected data about hand movements, eye movements and navigation paths within scenes, which could potentially be used for training data in robots (e.g. to train robot arm-suction grip, visual input and navigation respectively). I see projects like this https://aihabitat.org/, where my research seems quite relevant.
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Virtual environment frameworks
I need an easy to set up simulation of a 3d environment and I was wondering what you guys are using. Something like https://aihabitat.org/ . It already comes with rich visuals, which is quite important in my case and it works out of the box so I don't need to waste time developing my own models and graphics. Unfortunately habitat ai doesn't work on windows. Are there some alternatives?
- [D] Have we stopped researching agents?
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[D] Looking for open source projects to contribute
There are plenty of them out there. I spend a lot of time contributing to open source projects like Habitat-Sim https://github.com/facebookresearch/habitat-sim and Habitat-Lab https://github.com/facebookresearch/habitat-lab which have a ton of open issues and code maintaince stuff that we would welcome contributions of.
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[R] Best drone simulator for ML purposes
With some hacks it is pretty easy to get drones working in Habitat-Sim: https://github.com/facebookresearch/habitat-sim
bootcamp
- FLaNK AI - 01 April 2024
- FLaNK Stack Weekly 22 January 2024
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Milvus Adventures Jan 5, 2023
Metadata Filtering with Zilliz Cloud Pipelines This tutorial discuss scalar or metadata filtering and how you can perform metadata filtering in Zilliz Cloud. This blog continues on the previous blog on Getting started with RAG in just 5 minutes. You can find its code in this notebook and scroll down to Cell #27.
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Build a search engine, not a vector DB
Partially agree.
Vector DBs are critical components in retrieval systems. What most applications need are retrieval systems, rather than building blocks of retrieval systems. That doesn't mean the building blocks are not important.
As someone working on vector DB, I find many users struggling in building their own retrieval systems with building blocks such as embedding service (openai,cohere), logic orchestration framework (langchain/llamaindex) and vector databases, some even with reranker models. Putting them together is not as easy as it looks. A fairly changeling system work. Letting alone quality tuning and devops.
The struggle is no surprise to me, as tech companies who are experts on this (google,meta) all have dedicated teams working on retrieval system alone, making tons of optimizations and develop a whole feedback loop of evaluating and improving the quality. Most developers don't get access to such resource.
No one size fits all. I think there shall exist a service that democratize AI-powered retrieval, in simple words the know-how of using embedding+vectordb and a bunch of tricks to achieve SOTA retrieval quality.
With this idea I built a Retrieval-as-a-service solution, and here is its demo:
https://github.com/milvus-io/bootcamp/blob/master/bootcamp/R...
Curious to learn your thoughts.
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Vector Database in a Jupyter Notebook
Although it's common to use vector databases in conjunction with LLMs, I like to talk about vector databases in the context of unstructured data, i.e. any data that you can vectorize with (or without) an ML model. Yes, this includes text, but it also includes things like visual data, molecular structures, and geospatial data.
For folks who want to learn a bit more, there are examples of vector database use cases beyond semantic text search in our bootcamp: https://github.com/milvus-io/bootcamp
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Beginner-ish resources for choosing a vector database?
Easy to get started: Here are some tutorials for Milvus in a Jupyter Notebook that I wrote - reverse image search, semantic text search
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Semantic Similarity Search
I think you can just store your vector embeddings in the vector store somewhere and then query with your second document. I created a short tutorial on this that shows how to get the top 2 vector embeddings from a text query
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[D] Looking for open source projects to contribute
For more beginner tasks associated with the Milvus vector database, you can contribute to the Bootcamp project( https://github.com/milvus-io/bootcamp), where we build a lot of data-driven solutions using ML and Milvus vector database, including reverse image search, recommender systems, etc.
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I built an image similarity search system... Suggestions needed: what are some fun image datasets or scenarios I can use with this? :)
Source code here: https://github.com/milvus-io/bootcamp/tree/master/solutions/reverse_image_search
- Faiss: Facebook's open source vector search library
What are some alternatives?
gazebo-classic - Gazebo classic. For the latest version, see https://github.com/gazebosim/gz-sim
Milvus - A cloud-native vector database, storage for next generation AI applications
carla - Open-source simulator for autonomous driving research.
google-research - Google Research
nn - 🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
docarray - Represent, send, store and search multimodal data
es-clip-image-search - Sample implementation of natural language image search with OpenAI's CLIP and Elasticsearch or Opensearch.
habitat-lab - A modular high-level library to train embodied AI agents across a variety of tasks and environments.
annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
redtail - Perception and AI components for autonomous mobile robotics.