This is a cleaned version of the Quora dataset that's been configured with a train-test-val split.

Train : For training model Test : For running experiments and comparing different OSS models and closed sourced models Val : Only to be used at the end!

Colab Notebook to reproduce : https://colab.research.google.com/drive/1dGjGiqwPV1M7JOLfcPEsSh3SC37urItS?usp=sharing

Dataset

This dataset was created by GIOPAIK

Released under CC BY-SA 4.0

Contents

Context The Caltech-256 dataset is a foundational benchmark for object recognition, containing 30,607 images across 257 categories (256 object categories + 1 clutter category).

The original dataset is typically provided as a collection of directories, one for each category. This version streamlines the machine learning workflow by providing:

A clean, pre-defined 80/20 train-test split.

Manifest files (train.csv, test.csv) that map image paths directly to their labels, allowing for easy use with data generators in frameworks like PyTorch and TensorFlow.

A flat directory structure (train/, test/) for simplified file access.

File Content The dataset is organized into a single top-level folder and two CSV files:

train.csv: A CSV file containing two columns: image_path and label. This file lists all images designated for the training set.

test.csv: A CSV file with the same structure as train.csv, listing all images designated for the testing set.

Caltech-256_Train_Test/: The primary data folder.

train/: This directory contains 80% of the images from all 257 categories, intended for model training.

test/: This directory contains the remaining 20% of the images from all categories, reserved for model evaluation.

Data Split The dataset has been thoroughly partitioned to create a standard 80% training and 20% testing split. This split is (or should be assumed to be) stratified, meaning that each of the 257 object categories is represented in roughly an 80/20 proportion in the respective sets.

Acknowledgements & Original Source This dataset is a derivative work created for convenience. The original data and images belong to the authors of the Caltech-256 dataset.

Original Dataset Link: https://www.kaggle.com/datasets/jessicali9530/caltech256/data

Citation: Griffin, G. Holub, A.D. Perona, P. (2007). Caltech-256 Object Category Dataset. California Institute of Technology.

bcsandlund/arc-agi-prompts-train-test-split dataset hosted on Hugging Face and contributed by the HF Datasets community

2084Collective/deepstock-sp500-companies-info-stonkv2-test-train-split dataset hosted on Hugging Face and contributed by the HF Datasets community

Train Test Split For Freiburg Dataset In YOLOv7 Format

## Overview

Train Test Split For Freiburg Dataset In YOLOv7 Format is a dataset for object detection tasks - it contains Groceries annotations for 8,879 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Splits of train, test, and validation samples for Urban dataset.

Advances in neuroimaging, genomic, motion tracking, eye-tracking and many other technology-based data collection methods have led to a torrent of high dimensional datasets, which commonly have a small number of samples because of the intrinsic high cost of data collection involving human participants. High dimensional data with a small number of samples is of critical importance for identifying biomarkers and conducting feasibility and pilot work, however it can lead to biased machine learning (ML) performance estimates. Our review of studies which have applied ML to predict autistic from non-autistic individuals showed that small sample size is associated with higher reported classification accuracy. Thus, we have investigated whether this bias could be caused by the use of validation methods which do not sufficiently control overfitting. Our simulations show that K-fold Cross-Validation (CV) produces strongly biased performance estimates with small sample sizes, and the bias is still evident with sample size of 1000. Nested CV and train/test split approaches produce robust and unbiased performance estimates regardless of sample size. We also show that feature selection if performed on pooled training and testing data is contributing to bias considerably more than parameter tuning. In addition, the contribution to bias by data dimensionality, hyper-parameter space and number of CV folds was explored, and validation methods were compared with discriminable data. The results suggest how to design robust testing methodologies when working with small datasets and how to interpret the results of other studies based on what validation method was used.

varun500/healthbench-train-test-split dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by Hoàng Anh Nguyễn

Released under Other (specified in description)

Contents

Dataset

This dataset was created by KevinKSU

Released under CC0: Public Domain

Contents

The AUC of the precision and recall curve is shown (AUC with feature selection) for training and testing with the most important ROIs and with all ROIs (AUC without feature selection).

Model comparison using multiple metrics before balancing by SMOTE(Train-Test Split (80%-20%)).

Dataset

This dataset was created by Abdul Hasib Uddin

Contents

Many e-shops have started to mark-up product data within their HTML pages using the schema.org vocabulary. The Web Data Commons project regularly extracts such data from the Common Crawl, a large public web crawl. The Web Data Commons Training and Test Sets for Large-Scale Product Matching contain product offers from different e-shops in the form of binary product pairs (with corresponding label “match” or “no match”) for four product categories, computers, cameras, watches and shoes. In order to support the evaluation of machine learning-based matching methods, the data is split into training, validation and test sets. For each product category, we provide training sets in four different sizes (2.000-70.000 pairs). Furthermore there are sets of ids for each training set for a possible validation split (stratified random draw) available. The test set for each product category consists of 1.100 product pairs. The labels of the test sets were manually checked while those of the training sets were derived using shared product identifiers from the Web weak supervision. The data stems from the WDC Product Data Corpus for Large-Scale Product Matching - Version 2.0 which consists of 26 million product offers originating from 79 thousand websites. For more information and download links for the corpus itself, please follow the links below.

Metrics include sensitivity (Sens), specificity (Spec), positive predictive value (PPV), negative predictive value (NPV), F1, and accuracy (Acc), all shown as percentages. The best scores for each metric are shown in bold, and the final column presents differences in accuracy between each of the models and the most accurate model, the NB-SVM. Simultaneous confidence intervals are multiplicity-adjusted to control FWER.

Dataset

This dataset was created by Mohammad Abdul Basit

Contents

Sadhana-24/low_alt_satellite_image_dataset_500-train-test-split dataset hosted on Hugging Face and contributed by the HF Datasets community

Data split for each class of each dataset for training and test.

Dataset

This dataset was created by Dewizzz

Contents