unet
cellpose
| unet | cellpose | |
|---|---|---|
| 1 | 1 | |
| 4,445 | 1,181 | |
| - | 4.0% | |
| 0.0 | 9.0 | |
| 26 days ago | 27 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | 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.
unet
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U-net Returning Black Square As Prediction
Issue with the model architecture and what I’m trying to do with it? I’ve used the model from this GitHub project (https://github.com/zhixuhao/unet), as it seemed somewhat similar to what I was trying to do. I understand what is happening from layer to layer, but not the input/output parts. (I’ll put the code for it below)
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
What are some alternatives?
Human-Segmentation-PyTorch - Human segmentation models, training/inference code, and trained weights, implemented in PyTorch
rankseg - [JMLR 2023] RankSEG: A consistent ranking-based framework for segmentation
segmentation_models - Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
ETCI-2021-Competition-on-Flood-Detection - Experiments on Flood Segmentation on Sentinel-1 SAR Imagery with Cyclical Pseudo Labeling and Noisy Student Training
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
drrmsan - DRRMSAN: Deep Residual Regularized Multi-Scale Attention Networks for segmentation of medical images. Machine Leaning 2 (DA330) Course Project, RKMVERI.
hashformers - Hashformers is a framework for hashtag segmentation with Transformers and Large Language Models (LLMs).
Deep-Learning-In-Production - Build, train, deploy, scale and maintain deep learning models. Understand ML infrastructure and MLOps using hands-on examples.
SipMask - SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation (ECCV2020)
BCDU-Net - BCDU-Net : Medical Image Segmentation
segment-anything-with-clip - Segment Anything combined with CLIP