learnopencv
fastMONAI
| learnopencv | fastMONAI | |
|---|---|---|
| 6 | 1 | |
| 20,428 | 91 | |
| - | - | |
| 8.6 | 8.1 | |
| 3 days ago | 7 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| - | Apache License 2.0 |
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?
fastMONAI
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fastMONAI: a low-code deep learning library for medical image analysis
We have released version 0.1 of fastMONAI, a low-code Python-based deep learning library for medical imaging built on top of fastai, MONAI, and TorchIO. We created the library to simplify the use of state-of-the-art deep learning techniques in 3D medical image analysis for solving classification, regression, and segmentation tasks. The entire library is written using nbdev, a tool for exploratory programming that allows you to write, test, and document a Python library in Jupyter Notebooks. You can install the library using pip and download the tutorial notebooks here: https://github.com/MMIV-ML/fastMONAI. If you decide to try it out and discover any issues, please don’t hesitate to reach out!
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