cellpose
ETCI-2021-Competition-on-Flood-Detection
| cellpose | ETCI-2021-Competition-on-Flood-Detection | |
|---|---|---|
| 1 | 1 | |
| 1,198 | 150 | |
| 5.3% | - | |
| 9.0 | 1.8 | |
| 4 days ago | almost 2 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| BSD 3-clause "New" or "Revised" License | 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.
cellpose
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Meta introduced SAM, how can this change the game?
I've used cellpose in the past to segmebt cells with quite astonishing accurracy. https://github.com/MouseLand/cellpose
ETCI-2021-Competition-on-Flood-Detection
What are some alternatives?
unet - unet for image segmentation
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
rankseg - [JMLR 2023] RankSEG: A consistent ranking-based framework for segmentation
u-track - Multiple-particle tracking designed to (1) track dense particle fields, (2) close gaps in particle trajectories resulting from detection failure, and (3) capture particle merging and splitting events resulting from occlusion or genuine aggregation and dissociation events
TTS - :robot: :speech_balloon: Deep learning for Text to Speech (Discussion forum: https://discourse.mozilla.org/c/tts)
hashformers - Hashformers is a framework for hashtag segmentation with Transformers and Large Language Models (LLMs).
Pseudo-Labelling - Pseudo Labelling on MNIST dataset in Tensorflow 2.x