The data are also in the mlbench R package.Welcome to our Glass Reference Database! This is where all our sold items end up - instead of just deleting them, we keep them here for reference purposes. The predictors include 9 predictors, including the refractive index and percentages of 8 elements. The data consist of 214 glass samples labeled as one of seven class categories. Glass identification data: The UC Irvine Machine Learning Repository contains a data set related to glass identification. Download the entire archdata package and use the RBGlass2 dataset for trace elements. which is still the primary typology used for the identification of Roman Republican coin. An Adaptive Metric Machine for Pattern Classification.These open access Roman datasets can be reused for research purposes to inform or validate computational models in Roman studies. 64 samples were used as a Carlotta Domeniconi and Jing Peng and Dimitrios Gunopulos. The data set contains 214 samples with 10 attributes. # Glass identification data set - a data describing the material concentrations in glasses, with a class attribute denoting the type of the glass. Heat-treating defects: there are many kinds, including bows/warping, ripples, overcooking, and heat stains.
Bad aris: the aris sizes on both sides don’t match.
Miss spots: an indent in an otherwise flat polished edge. Examples include: Breakouts: waves along the edge of the glass caused by the cutting process. The training set features 67,692 images (one fruit or vegetable per image. Fruits 360 – This dataset features 90,483 images of different fruits and vegetables.
The dataset features over 1,000 images across 10 separate categories including altar, column, dome (inner), dome (outer), stained glass, vault, flying buttress, apse, and bell tower.Fruits 360 - This dataset features 90,483 images of different fruits and vegetables. This challenge listed on Kaggle had 1,286 different teams participating.The dataset features over 1,000 images across 10 separate categories including altar, column, dome (inner), dome (outer), stained glass, vault, flying buttress, apse, and bell tower. Kaggle competitions are a great way to level up your Machine Learning skills and this tutorial will help you get comfortable with the way image data is formatted on the site. glass.csv: Glass Identification Data Set: heart_cleveland.csv: Heart Disease Data Set: hepatitis.csv: Hepatitis Data Set: horse-colic.csv: Horse Colic Data Set : housing.csv: Boston Housing Data Set:The dataset we are using is from the Dog Breed identification challenge on. These data sets can be used for class projects in my T81-558: Applications of Deep Learning for projects.