neuronflow/blob_loss
blob loss example implementation
This helps researchers in medical imaging and computer vision train more accurate models for semantic segmentation. When working with images where important features (like tumors or lesions) are small or rare, this tool improves how well models identify and outline them. It takes your image data and segmentation masks, and produces a more robust training outcome for your deep learning model.
No commits in the last 6 months.
Use this if you are developing computer vision models for tasks like medical image analysis where accurately segmenting small or sparse objects is critical.
Not ideal if you are looking for a highly optimized, production-ready solution, as this is an example implementation not optimized for speed.
Stars
36
Forks
2
Language
Python
License
MIT
Category
Last pushed
Aug 03, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/neuronflow/blob_loss"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
HelmchenLabSoftware/Cascade
Calibrated inference of spiking from calcium ΔF/F data using deep networks
adobe/antialiased-cnns
pip install antialiased-cnns to improve stability and accuracy
KaiyangZhou/pytorch-center-loss
Pytorch implementation of Center Loss
kimhc6028/relational-networks
Pytorch implementation of "A simple neural network module for relational reasoning" (Relational Networks)
devsisters/pointer-network-tensorflow
TensorFlow implementation of "Pointer Networks"