aquamam
ailia-models
| aquamam | ailia-models | |
|---|---|---|
| 3 | 4 | |
| 1 | 1,840 | |
| - | 3.2% | |
| 3.6 | 9.8 | |
| 8 months ago | 6 days ago | |
| Python | Python | |
| MIT License | - |
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.
aquamam
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DeepVoxels: Learning Persistent 3D Feature Embeddings
This paper is near and dear to my heart because I saw the presentation at the first and only machine learning conference I've ever attended (thanks a lot, ICML, NeurIPS (x2), and ICLR!). It's a neural rendering approach that precedes NeRF, but you can see some similarities (even more so in the follow-up paper about "Scene Representation Networks"). Sitzmann and co-authors also published a paper about using sinusoidal activations in implicit representation models at NeurIPS 2020, the same conference where the "Fourier Features" paper (which has many of the same authors as the NeRF paper) was also presented. It's always interesting to me to see how ideas in science often pop-up at the same time from different researchers (e.g., attention).
- AQuaMaM: An Autoregressive, Quaternion Manifold Model for Rapidly Estimating Complex SO(3) Distributions
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[R] AQuaMaM: An Autoregressive, Quaternion Manifold Model for Rapidly Estimating Complex SO(3) Distributions
Code for the paper can be found here.
ailia-models
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10$ Full Body Tracking! I'm proud to release ToucanTrack (in Beta!). Get decent FBT with the power of 2 PS3 Eye Cameras and AI!
If you're looking for the differences in terms of how inference is done, I recommend you take a look at MediaPipe's source code. MediaPipe doesn't use raw code, but uses a "graph" instead (eg. pose_landmark_cpu.pbtxt), which can be visualised using MediaPipe Viz. I also used axinc-ai/ailia-models as the base (preprocessing, inference, postprocessing, etc...) which I further built upon (keypoint refinement, roi from keypoints, filtering / smoothing, etc...)
- [P] A collection of pre-trained, state-of-the-art AI models
- Ailia-models: A collection of pre-trained, state-of-the-art AI models
What are some alternatives?
openpifpaf - Official implementation of "OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association" in PyTorch.
DeepCamera - Open-Source AI Camera. Empower any camera/CCTV with state-of-the-art AI, including facial recognition, person recognition(RE-ID) car detection, fall detection and more
Hierarchical-Localization - Visual localization made easy with hloc
tensorflow-onnx - Convert TensorFlow, Keras, Tensorflow.js and Tflite models to ONNX
baller2vec - A multi-entity Transformer for multi-agent spatiotemporal modeling.
Put-In-Context - Putting Visual Object Recognition in Context
SuperGluePretrainedNetwork - SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)
robotics-level-4 - This repo contains projects created using TensorFlow-Lite on Raspberry Pi and Teachable Machine. AI and ML capabilities have been integrated with Robot's software.
baller2vecplusplus - A look-ahead multi-entity Transformer for modeling coordinated agents.
mlapi - An easy to use/extend object recognition API you can locally install. Python+Flask. Also works with ZMES!
DeepLabCut - Official implementation of DeepLabCut: Markerless pose estimation of user-defined features with deep learning for all animals incl. humans
PeekingDuck - A modular framework built to simplify Computer Vision inference workloads.