The WildDet3D dataset includes millions of 3D bounding boxes with depth maps and camera parameters across 11,000+ categories, providing a substantial resource for training and evaluating AI models in 3D perception tasks. Senior engineers may find this dataset valuable for enhancing their AI systems with rich 3D data.
Allen AI just released the WildDet3D dataset on Hugging Face
millions of 3D bounding boxes
with depth maps and camera parameters
across 11,000+ categories
from COCO, LVIS and more.
This tweet announces the open-sourcing of a core framework for Quantum AI, built in JAX with GPU/TPU support. Senior engineers may find the actual code and implementation useful for experimentation and development.
To bridge theory and practice, we are open-sourcing our core framework. Our numerical implementation is built in JAX (with native GPU/TPU acceleration).
Check out the code, run the simulations, and help us shape the future of Quantum AI at
The code for Covenant-72B is fully open-source on GitHub, and the model weights are available on Hugging Face. This allows engineers to fork and utilize the resources immediately, which is relevant for those looking to build on existing AI infrastructure.
Sam Dare can take his team and walk.
He can’t take what actually made Covenant-72B possible.
The code? Fully open-source. Templar repo on GitHub (MIT license) — anyone can fork it today.
Covenant-72B weights? Apache 2.0 on Hugging Face. Download it right now.
But the
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