learnopencv
lava-dl
| learnopencv | lava-dl | |
|---|---|---|
| 6 | 1 | |
| 20,536 | 140 | |
| - | 5.0% | |
| 8.6 | 7.8 | |
| 8 days ago | 6 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| - | BSD 3-clause "New" or "Revised" 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.
learnopencv
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YOLO-NAS Pose
Deci's YOLO-NAS Pose: Redefining Pose Estimation! Elevating healthcare, sports, tech, and robotics with precision and speed. Github link and blog link down below! Repo: https://github.com/spmallick/learnopencv/tree/master/YOLO-NAS-Pose
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Bidirectional Encoder Representations from Transformers
Discover the transformative influence of BERT (Bidirectional-Encoder-Representations-from-Transformers) on Natural Language Processing! Repo: https://github.com/spmallick/learnopencv/tree/master/BERT-Bidirectional-Encoder-Representations-from-Transformers Read: https://learnopencv.com/bert-bidirectional-encoder-representations-from-transformers/
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Diving deeper into KerasCV!
Read: https://learnopencv.com/comparing-kerascv-yolov8-models/ Repo: https://github.com/spmallick/learnopencv/tree/master/Comparing-KerasCV-YOLOv8-Models-on-the-Global-Wheat-Data-2020
- write a program to convert RGB to HSV and LAB color space.
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Object detection with depth measurement using pre-trained models with OAK-D
Code Link : https://github.com/spmallick/learnopencv/tree/master/OAK-Object-Detection-with-Depth
- [Question] How does one get all available coordinates in a color marked contour?
lava-dl
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Has anyone used Spiking Neural Networks (SNNs) for image processing?
Surrogate gradient learning w/ backpropagation: for short, you can use backpropagation with SNNs (by a little trick during the backward pass). Super easy to implement, super efficient. You have a deep SNN trained via backprop with any type of input you want. Personally, that is completely my jam. Maybe you can use such paradigm to easily train an SNN in your biomed image dataset. Good repos: SnnTorch comes with the best tutorials to explain SNNs and surrogate gradient learning. This is the fastest way to understand the field and begin to implement you solution. Nevertheless, spikingjelly remains a better option when it comes to implement your ideas (better memory efficiency, etc). Good mention to lava-dl, with which you can train a neural network and directly transfer it into neuromorphic hardware (Intel Loihi) if you have access to this kind of chip.
What are some alternatives?
conformal_classification - Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true class with high probability (via conformal prediction).
spikingjelly - SpikingJelly is an open-source deep learning framework for Spiking Neural Network (SNN) based on PyTorch.
Human-pose-estimation - A quick tutorial on multi-pose estimation with OpenCV, Tensorflow and MoveNet lightning.
rtdl-revisiting-models - (NeurIPS 2021) Revisiting Deep Learning Models for Tabular Data
YOLOv3-Cloud-Based-Fire-Detection - Custom Object detection using YOLOv3 on the cloud. It is trained to detect Fire in a given frame. It can be largely used for Wildfires, fire accidents, etc.
shap - A game theoretic approach to explain the output of any machine learning model.
xojo-opencvc - Xojo-OpenCVC brings OpenCV 4.5+ to Xojo, using the OpenCV-C API
Best_AI_paper_2020 - A curated list of the latest breakthroughs in AI by release date with a clear video explanation, link to a more in-depth article, and code
F1_Quali_Prediction - Finding explainable models to predict Formula 1 Qualifying Results
fastMONAI - Simplifying deep learning for medical imaging
lane-detection-opencv - This is for detecting any lane in the road to identify the road map
Deep-Learning-In-Production - Build, train, deploy, scale and maintain deep learning models. Understand ML infrastructure and MLOps using hands-on examples.