This repository represents a web app with a multi-class classification ML model which creates a segmented image of rocks and plain land.
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Updated
Mar 3, 2024 - Jupyter Notebook
This repository represents a web app with a multi-class classification ML model which creates a segmented image of rocks and plain land.
"This program trains a model using 'SVM' or 'Softmax' and predicts the input data. Loss history and predicted tags are displayed as results."
This repository contains the implementation for our work "Learning Topological Interactions for Multi-Class Medical Image Segmentation", accepted to ECCV 2022 (Oral)
Discord Bot for computing your AL D&D 5e character's hit points, given the Constitution modifier, its classes and levels, and other HP modifiers such as Tough feat or being a Hill Dwarf.
This repository contains Python code for rice type detection using multiclass classification. The project leverages the MobileNetV2 architecture to classify six different types of rice: Arborio, Basmati, Ipsala, Jasmine, and Karacadag. The dataset used for training and evaluation can be found on Kaggle and consists of categorized rice images.
We investigated the performance of the Logistic and Multiclass Regression models and compared their accuracies to KNN. We compared Logistic Regression and KNN based on the "IMdB reviews" dataset, while Multiclass Regression and KNN were compared based on the "20 news groups" dataset.
Multi-class metrics for Tensorflow
This is an implementation of multi-class focal loss in PyTorch.
A hyperspectral data set can be used for testing binary and multi-class change detection techniques.
A hyperspectral data set can be used for testing binary and multi-class change detection techniques.
LAMA - automatic model creation framework
Applying K Means and KNN on a multiclass dataset to make clusters and find nearest neighbours.
Experiments with UNET/FPN models and cityscapes/kitti datasets [Pytorch]
Fast MOT base on yolo+deepsort, support yolo3 and yolo4
A jupyter notebook for analyzing and estimating with sklearn a multiclass classification problem for the Machine Learning (DD2421) course
I am interested in exploring machine learning techniques to predict the outcome of a European soccer game using game and player information collected. The generated predictive model will be able to predict the outcome of a game as Win, Loss or Draw for the home team. This model will use various attributes that are calculated each year for both h…
performance metrics for multi class in machine learning with codes
Pseudo-Inverse, Gradient-Stochastic-Steepest Descent, Logistic Regression and LDA-QDA
In this project tutorial we will discover how we can use Keras to develop and evaluate neural network models for multiclass classification problems
Repository for KDD-Cup 2019 with Baidu. Big Data Science practical course @ LMU
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