habitat-sim
nn
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| habitat-sim | nn | |
|---|---|---|
| 5 | 26 | |
| 2,351 | 47,503 | |
| 2.6% | 7.6% | |
| 8.8 | 7.7 | |
| about 16 hours ago | 26 days ago | |
| C++ | Jupyter Notebook | |
| MIT License | MIT License |
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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
nn
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Can't remember name of website that has explanations side-by-side with code
Hey are you talking about https://nn.labml.ai/ ?
- [D] Recent ML papers to implement from scratch
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[P] GPT-NeoX inference with LLM.int8() on 24GB GPU
Implementation & LM Eval Harness Results
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[P] Fine-tuned the GPT-Neox Model to Generate Quotes
Github: https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/neox
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Best resources to learn recent transformer papers and stay updated [D]
Regarding implementations this helps me: https://nn.labml.ai/
- Introductory papers to implement
- How to convert research papers to code?
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[D] How to convert papers to code?
Dunno if this is directly helpful, but this website has implementation with the math side by side https://nn.labml.ai/
- [D] Looking for open source projects to contribute
- Resource for papers explanation
What are some alternatives?
gazebo-classic - Gazebo classic. For the latest version, see https://github.com/gazebosim/gz-sim
GFPGAN-for-Video-SR - A colab notebook for video super resolution using GFPGAN
carla - Open-source simulator for autonomous driving research.
labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
bootcamp - Dealing with all unstructured data, such as reverse image search, audio search, molecular search, video analysis, question and answer systems, NLP, etc.
functorch - functorch is JAX-like composable function transforms for PyTorch.
habitat-lab - A modular high-level library to train embodied AI agents across a variety of tasks and environments.
ZoeDepth - Metric depth estimation from a single image
docarray - Represent, send, store and search multimodal data
onnx-simplifier - Simplify your onnx model
redtail - Perception and AI components for autonomous mobile robotics.
Basic-UI-for-GPT-J-6B-with-low-vram - A repository to run gpt-j-6b on low vram machines (4.2 gb minimum vram for 2000 token context, 3.5 gb for 1000 token context). Model loading takes 12gb free ram.