all kinds of text classification models and more with deep learning
Real time one-stage multi-class & multi-object tracking based on anchor-free detection and ReID
Semantic Affinity-Aware Weakly Supervised Learning for Multi-Class Medical Image Segmentation with Slice-Level Labels
Better multi-class confusion matrix plots for Scikit-Learn, incorporating per-class and overall evaluation measures.
Toolbox for interpolating from an uneven grid
Predict health outcomes based on sleep, health, and lifestyle factors using supervised machine learning models (Logistic Regression, Decision Tree, Random Forest). Includes feature importance analysis and performance evaluation.
Multi-class Segmentation Examples with U-Net.
Predict health outcomes based on sleep, health, and lifestyle factors using supervised machine learning models (Logistic Regression, Decision Tree, Random Forest). Includes feature importance analysis and performance evaluation.
An easy-to-use multi-label image dataset generator.
Multi-class bounding box labeling tool
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