cellpose VS hashformers

Compare cellpose vs hashformers and see what are their differences.

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cellpose hashformers
1 2
1,198 63
5.3% -
9.0 6.2
3 days ago 12 months ago
Jupyter Notebook Python
BSD 3-clause "New" or "Revised" License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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

Posts with mentions or reviews of cellpose. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-04-06.

hashformers

Posts with mentions or reviews of hashformers. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing cellpose and hashformers you can also consider the following projects:

unet - unet for image segmentation

Twitter-sentiment-analysis - A sentiment analysis model trained with Kaggle GPU on 1.6M examples, used to make inferences on 220k tweets about Messi and draw insights from their results.

rankseg - [JMLR 2023] RankSEG: A consistent ranking-based framework for segmentation

NewsMTSC - Target-dependent sentiment classification in news articles reporting on political events. Includes a high-quality data set of over 11k sentences and a state-of-the-art classification model.

ETCI-2021-Competition-on-Flood-Detection - Experiments on Flood Segmentation on Sentinel-1 SAR Imagery with Cyclical Pseudo Labeling and Noisy Student Training

FinBERT - A Pretrained BERT Model for Financial Communications. https://arxiv.org/abs/2006.08097

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

should-i-follow - 🦄 An NLP application just for the lols: built with Haystack to get an overview of what a user is posting about on Twitter