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.

Occluded Validation Set Cropped

## Overview

Occluded Validation Set Cropped is a dataset for object detection tasks - it contains Sheep annotations for 243 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).

The dataset used in the paper is the ImageNet validation set, a subset of the ImageNet dataset.

A dataset used to train and test the neural network classifiers.

Tomato Validation Set

## Overview

Tomato Validation Set is a dataset for object detection tasks - it contains Tomato annotations for 4,265 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).

Dataset

This dataset was created by Anna

Contents

Five models are trained using various input masking probabilities (IMP). Each resulting model is validated using the heavily masked validation dataset of 13596 samples (5668 positive) to evaluate their performance in the context of missing input data. AUC values for the optimal training IMP are shown, along with those achieved with no input masking (NIM). Bold font indicates the highest AUC in the table. Results for other IMP values are provided in the S1 File.

The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center in Sioux Falls, SD developed a cloud validation dataset from 48 unique Landsat 8 Collection 1 images. These images were selected at random from the Landsat 8 archive from various locations around the world. While these validation images were subjectively designed by a single analyst, they provide useful information for quantifying the accuracy of clouds flagged by various cloud masking algorithms. Each mask is provided in GeoTIFF format, and includes all bands from the original Landsat 8 Level-1 Collection 1 data product (COG GeoTIFF), and its associated Level-1 metadata (MTL.txt file).

Validation Set

## Overview

Validation Set is a dataset for object detection tasks - it contains Cars Motorcycles annotations for 219 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).

The MS Training Set, MS Validation Set, and UW Validation/Test Set are used for training, validation, and testing the proposed methods.

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.

Original dataset here - https://huggingface.co/datasets/KbsdJames/Omni-MATH

Processed by selecting only INT solutions of 7+ difficulty. Then ran through COT and TIR with Qwen2.5-math-1.5B-instruct and further processed by filtering out any problem solved or common problems solved by this model.

taesiri/IERv2-Validation-Set dataset hosted on Hugging Face and contributed by the HF Datasets community

The dataset used in the paper is a validation set from one discharge, containing N-channel MUM system samples.

val_half: containing 1/4 ids which have 50% of pictures in this validation set and 50% in the training set

val_all: containing 1/4 ids whose pictures are not included in the training set

train: training set

test: test set

The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center in Sioux Falls, SD developed a cloud validation dataset from 48 unique Landsat 9 Collection 2 images. These images were selected at random from the Landsat 9 archive from various locations around the world. While these validation images were subjectively designed by a single analyst, they provide useful information for quantifying the accuracy of clouds flagged by various cloud masking algorithms. Each mask is provided in GeoTIFF format, and includes all bands from the original Landsat 9 Collection 2 Level-1 data product (COG GeoTIFF), and its associated Level-1 metadata (MTL.txt file).

Validation Set

## Overview

Validation Set is a dataset for object detection tasks - it contains Cars Trucks Vans Pedestrians annotations for 1,500 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 [MIT license](https://creativecommons.org/licenses/MIT).

Validation Data Set

## Overview

Validation Data Set is a dataset for object detection tasks - it contains Microscopic Eggs annotations for 300 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).

Comparison of classification results of different models on the validation set.

For each validation set the following metrics were calculated: RMSE, Pearson’s correlation coefficient, proportion of predictions exceeding the OOS estimates, and average absolute error (average of the absolute value of the difference between predicted and actual raptor average probability of persistence).