Dataset Card for Alpaca

I have just performed train, test and validation split on the original dataset. Repository to reproduce this will be shared here soon. I am including the orignal Dataset card as follows.

  Dataset Summary

Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's text-davinci-003 engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.… See the full description on the dataset page: https://huggingface.co/datasets/disham993/alpaca-train-validation-test-split.

Dataset, splits, models, and scripts from the manuscript "When Do Quantum Mechanical Descriptors Help Graph Neural Networks Predict Chemical Properties?" are provided. The curated dataset includes 37 QM descriptors for 64,921 unique molecules across six levels of theory: wB97XD, B3LYP, M06-2X, PBE0, TPSS, and BP86. This dataset is stored in the data.tar.gz file, which also contains a file for multitask constraints applied to various atomic and bond properties. The data splits (training, validation, and test splits) for both random and scaffold-based divisions are saved as separate index files in splits.tar.gz. The trained D-MPNN models for predicting QM descriptors are saved in the models.tar.gz file. The scripts.tar.gz file contains ready-to-use scripts for training machine learning models to predict QM descriptors, as well as scripts for predicting QM descriptors using our trained models on unseen molecules and for applying radial basis function (RBF) expansion to QM atom and bond features.

Below are descriptions of the available scripts:

1. atom_bond_descriptors.sh: Trains atom/bond targets.
2. atom_bond_descriptors_predict.sh: Predicts atom/bond targets from pre-trained model.
3. dipole_quadrupole_moments.sh: Trains dipole and quadrupole moments.
4. dipole_quadrupole_moments_predict.sh: Predicts dipole and quadrupole moments from pre-trained model.
5. energy_gaps_IP_EA.sh: Trains energy gaps, ionization potential (IP), and electron affinity (EA).
6. energy_gaps_IP_EA_predict.sh: Predicts energy gaps, IP, and EA from pre-trained model.
7. get_constraints.py: Generates constraints file for testing dataset. This generated file needs to be provided before using our trained models to predict the atom/bond QM descriptors of your testing data.
8. csv2pkl.py: Converts QM atom and bond features to .pkl files using RBF expansion for use with Chemprop software.

Below is the procedure for running the ml-QM-GNN on your own dataset:

1. Use get_constraints.py to generate a constraint file required for predicting atom/bond QM descriptors with the trained ML models.
2. Execute atom_bond_descriptors_predict.sh to predict atom and bond properties. Run dipole_quadrupole_moments_predict.sh and energy_gaps_IP_EA_predict.sh to calculate molecular QM descriptors.
3. Utilize csv2pkl.py to convert the data from predicted atom/bond descriptors .csv file into separate atom and bond feature files (which are saved as .pkl files here).
4. Run Chemprop to train your models using the additional predicted features supported here.

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).

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

Node classification on Film with 60%/20%/20% random splits for training/validation/test.

Dataset

This dataset was created by IMT2022053

Released under Apache 2.0

Contents

Dataset

This dataset was created by pascalammeter

Released under MIT

Contents

The Dayton dataset is a dataset for ground-to-aerial (or aerial-to-ground) image translation, or cross-view image synthesis. It contains images of road views and aerial views of roads. There are 76,048 images in total and the train/test split is 55,000/21,048. The images in the original dataset have 354×354 resolution.

Overview

The Hard Hat dataset is an object detection dataset of workers in workplace settings that require a hard hat. Annotations also include examples of just "person" and "head," for when an individual may be present without a hard hart.

The original dataset has a 75/25 train-test split.

Example Image: https://i.imgur.com/7spoIJT.png" alt="Example Image">

Use Cases

One could use this dataset to, for example, build a classifier of workers that are abiding safety code within a workplace versus those that may not be. It is also a good general dataset for practice.

Using this Dataset

Use the fork or Download this Dataset button to copy this dataset to your own Roboflow account and export it with new preprocessing settings (perhaps resized for your model's desired format or converted to grayscale), or additional augmentations to make your model generalize better. This particular dataset would be very well suited for Roboflow's new advanced Bounding Box Only Augmentations.

Dataset Versions:

Image Preprocessing | Image Augmentation | Modify Classes * v1 (resize-416x416-reflect): generated with the original 75/25 train-test split | No augmentations * v2 (raw_75-25_trainTestSplit): generated with the original 75/25 train-test split | These are the raw, original images * v3 (v3): generated with the original 75/25 train-test split | Modify Classes used to drop person class | Preprocessing and Augmentation applied * v5 (raw_HeadHelmetClasses): generated with a 70/20/10 train/valid/test split | Modify Classes used to drop person class * v8 (raw_HelmetClassOnly): generated with a 70/20/10 train/valid/test split | Modify Classes used to drop head and person classes * v9 (raw_PersonClassOnly): generated with a 70/20/10 train/valid/test split | Modify Classes used to drop head and helmet classes * v10 (raw_AllClasses): generated with a 70/20/10 train/valid/test split | These are the raw, original images * v11 (augmented3x-AllClasses-FastModel): generated with a 70/20/10 train/valid/test split | Preprocessing and Augmentation applied | 3x image generation | Trained with Roboflow's Fast Model * v12 (augmented3x-HeadHelmetClasses-FastModel): generated with a 70/20/10 train/valid/test split | Preprocessing and Augmentation applied, Modify Classes used to drop person class | 3x image generation | Trained with Roboflow's Fast Model * v13 (augmented3x-HeadHelmetClasses-AccurateModel): generated with a 70/20/10 train/valid/test split | Preprocessing and Augmentation applied, Modify Classes used to drop person class | 3x image generation | Trained with Roboflow's Accurate Model * v14 (raw_HeadClassOnly): generated with a 70/20/10 train/valid/test split | Modify Classes used to drop person class, and remap/relabel helmet class to head

Choosing Between Computer Vision Model Sizes | Roboflow Train

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless.

Developers reduce 50% of their code when using Roboflow's workflow, automate annotation quality assurance, save training time, and increase model reproducibility.

SDC-Scissor tool for Cost-effective Simulation-based Test Selection in Self-driving Cars Software

This dataset provides test cases for self-driving cars with the BeamNG simulator. Check out the repository and demo video to get started.

GitHub: github.com/ChristianBirchler/sdc-scissor

This project extends the tool competition platform from the Cyber-Phisical Systems Testing Competition which was part of the SBST Workshop in 2021.

Usage

Demo

YouTube Link

Installation

The tool can either be run with Docker or locally using Poetry.

When running the simulations a working installation of BeamNG.research is required. Additionally, this simulation cannot be run in a Docker container but must run locally.

To install the application use one of the following approaches:

• Docker: docker build --tag sdc-scissor .
• Poetry: poetry install

Using the Tool

The tool can be used with the following two commands:

• Docker: docker run --volume "$(pwd)/results:/out" --rm sdc-scissor [COMMAND] [OPTIONS] (this will write all files written to /out to the local folder results)
• Poetry: poetry run python sdc-scissor.py [COMMAND] [OPTIONS]

There are multiple commands to use. For simplifying the documentation only the command and their options are described.

• Generation of tests:
  • generate-tests --out-path /path/to/store/tests
• Automated labeling of Tests:
  • label-tests --road-scenarios /path/to/tests --result-folder /path/to/store/labeled/tests
  • Note: This only works locally with BeamNG.research installed
• Model evaluation:
  • evaluate-models --dataset /path/to/train/set --save
• Split train and test data:
  • split-train-test-data --scenarios /path/to/scenarios --train-dir /path/for/train/data --test-dir /path/for/test/data --train-ratio 0.8
• Test outcome prediction:
  • predict-tests --scenarios /path/to/scenarios --classifier /path/to/model.joblib
• Evaluation based on random strategy:
  • evaluate --scenarios /path/to/test/scenarios --classifier /path/to/model.joblib

The possible parameters are always documented with --help.

Linting

The tool is verified the linters flake8 and pylint. These are automatically enabled in Visual Studio Code and can be run manually with the following commands:

poetry run flake8 .
poetry run pylint **/*.py

License

The software we developed is distributed under GNU GPL license. See the LICENSE.md file.

Contacts

Christian Birchler - Zurich University of Applied Science (ZHAW), Switzerland - birc@zhaw.ch

Nicolas Ganz - Zurich University of Applied Science (ZHAW), Switzerland - gann@zhaw.ch

Sajad Khatiri - Zurich University of Applied Science (ZHAW), Switzerland - mazr@zhaw.ch

Dr. Alessio Gambi - Passau University, Germany - alessio.gambi@uni-passau.de

Dr. Sebastiano Panichella - Zurich University of Applied Science (ZHAW), Switzerland - panc@zhaw.ch

References

• Christian Birchler, Nicolas Ganz, Sajad Khatiri, Alessio Gambi, and Sebastiano Panichella. 2022. Cost-effective Simulation-based Test Selection in Self-driving Cars Software with SDC-Scissor. In 2022 IEEE 29th International Conference on Software Analysis, Evolution and Reengineering (SANER), IEEE.

If you use this tool in your research, please cite the following papers:

@INPROCEEDINGS{Birchler2022,
 author={Birchler, Christian and Ganz, Nicolas and Khatiri, Sajad and Gambi, Alessio, and Panichella, Sebastiano},
 booktitle={2022 IEEE 29th International Conference on Software Analysis, Evolution and Reengineering (SANER), 
 title={Cost-effective Simulationbased Test Selection in Self-driving Cars Software with SDC-Scissor}, 
 year={2022},
}

Dataset Card for "stackoverflow_linux"

Dataset information:

Source: Stack Overflow Category: Linux Number of samples: 300 Train/Test split: 270/30 Quality: Data come from the top 1k most upvoted questions

  Additional Information





  License

All Stack Overflow user contributions are licensed under CC-BY-SA 3.0 with attribution required. More Information needed

Overview

The Hard Hat dataset is an object detection dataset of workers in workplace settings that require a hard hat. Annotations also include examples of just "person" and "head," for when an individual may be present without a hard hart.

The original dataset has a 75/25 train-test split.

Example Image: https://i.imgur.com/7spoIJT.png" alt="Example Image">

Use Cases

One could use this dataset to, for example, build a classifier of workers that are abiding safety code within a workplace versus those that may not be. It is also a good general dataset for practice.

Using this Dataset

Use the fork or Download this Dataset button to copy this dataset to your own Roboflow account and export it with new preprocessing settings (perhaps resized for your model's desired format or converted to grayscale), or additional augmentations to make your model generalize better. This particular dataset would be very well suited for Roboflow's new advanced Bounding Box Only Augmentations.

Dataset Versions:

Image Preprocessing | Image Augmentation | Modify Classes * v1 (resize-416x416-reflect): generated with the original 75/25 train-test split | No augmentations * v2 (raw_75-25_trainTestSplit): generated with the original 75/25 train-test split | These are the raw, original images * v3 (v3): generated with the original 75/25 train-test split | Modify Classes used to drop person class | Preprocessing and Augmentation applied * v5 (raw_HeadHelmetClasses): generated with a 70/20/10 train/valid/test split | Modify Classes used to drop person class * v8 (raw_HelmetClassOnly): generated with a 70/20/10 train/valid/test split | Modify Classes used to drop head and person classes * v9 (raw_PersonClassOnly): generated with a 70/20/10 train/valid/test split | Modify Classes used to drop head and helmet classes * v10 (raw_AllClasses): generated with a 70/20/10 train/valid/test split | These are the raw, original images * v11 (augmented3x-AllClasses-FastModel): generated with a 70/20/10 train/valid/test split | Preprocessing and Augmentation applied | 3x image generation | Trained with Roboflow's Fast Model * v12 (augmented3x-HeadHelmetClasses-FastModel): generated with a 70/20/10 train/valid/test split | Preprocessing and Augmentation applied, Modify Classes used to drop person class | 3x image generation | Trained with Roboflow's Fast Model * v13 (augmented3x-HeadHelmetClasses-AccurateModel): generated with a 70/20/10 train/valid/test split | Preprocessing and Augmentation applied, Modify Classes used to drop person class | 3x image generation | Trained with Roboflow's Accurate Model * v14 (raw_HeadClassOnly): generated with a 70/20/10 train/valid/test split | Modify Classes used to drop person class, and remap/relabel helmet class to head

Choosing Between Computer Vision Model Sizes | Roboflow Train

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless.

Developers reduce 50% of their code when using Roboflow's workflow, automate annotation quality assurance, save training time, and increase model reproducibility.

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

Dataset

This dataset was created by bharghav_kv_02

Contents

This benchmark data is comprised of 50 different datasets for materials properties obtained from 16 previous publications. The data contains both experimental and computational data, data suited for regression as well as classification, sizes ranging from 12 to 6354 samples, and materials systems spanning the diversity of materials research. In addition to cleaning the data where necessary, each dataset was split into train, validation, and test splits.

For datasets with more than 100 values, train-val-test splits were created, either with a 5-fold or 10-fold cross-validation method, depending on what each respective paper did in their studies. Datasets with less than 100 values had train-test splits created using the Leave-One-Out cross-validation method.

For further information, as well as directions on how to access the data, please go to the corresponding GitHub repository: https://github.com/anhender/mse_ML_datasets/tree/v1.0

This JSON file contains the ground truth annotations for the train and validation set of the DUDE competition (https://rrc.cvc.uab.es/?ch=23&com=tasks) of ICDAR 2023 (https://icdar2023.org/).

V1.0.7 release: 41454 annotations for 4974 documents (train-validation-test)

DatasetDict({
  train: Dataset({
    features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'],
    num_rows: 23728
  })
  val: Dataset({
    features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'],
    num_rows: 6315
  })
  test: Dataset({
    features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'],
    num_rows: 11402
  })
})

++update on answer_type
+++formatting change to answers_variants
++++stricter check on answer_variants & rename annotations file

+ blind test set (no ground truth answers provided)
++ removed duplicates from test set: 

"92bd5c758bda9bdceb5f67c17009207b_ac6964cbdf483e765b6668e27b3d0bc4",

"6ee71a16d4e4d1dbd7c1f569a92d4e08_549f2a163f8ff3e9f0293cf59fdd98bc",

"e6f3855472231a7ca6aada2f8e85fe5a_827c03a72f2552c722f2c872fd7f74c3",

"e3eecd7cca5de11f1d17cd94ae6a8d77_6300df64e4cf6ba0600ac81278f68de2",

"107b4037df8127a92ee4b6ae9b5df8fb_d7a60e7a9fc0b27487ea39cd7f56f98e",

"300cc3900080064d308983f958141232_6a7cf1aad908d58a75ab8e02ddc856f4",

"fdd3308efacddb88d4aa6e2073f481d4_138cb868ecc804a63cc7a4502c0009b2",

"1f7de256ff1743d329a8402ba0d132e7_95b6e8758533a9817b9f20a958e7b776",

"4f399b8c526ffb6a2fd585a18d4ed5ec_51097231bc327c26c59a4fd8d3ff3069",

This is an open source - publicly available dataset which can be found at https://shahariarrabby.github.io/ekush/ . We split the dataset into three sets - train, validation, and test. For our experiments, we created two other versions of the dataset. We have applied 10-fold cross validation on the train set and created ten folds. We also created ten bags of datasets using bootstrap aggregating method on the train and validation sets. Lastly, we created another dataset using pre-trained ResNet50 model as feature extractor. On the features extracted by ResNet50 we have applied PCA and created a tabilar dataset containing 80 features. pca_features.csv is the train set and pca_test_features.csv is the test set. Fold.tar.gz contains the ten folds of images described above. Those folds are also been compressed. Similarly, Bagging.tar.gz contains the ten compressed bags of images. The original train, validation, and test sets are in Train.tar.gz, Validation.tar.gz, and Test.tar.gz, respectively. The compression has been performed for speeding up the upload and download purpose and mostly for the sake of convenience. If anyone has any question about how the datasets are organized please feel free to ask me at shiblygnr@gmail.com .I will get back to you in earliest time possible.

The MLCommons Dollar Street Dataset is a collection of images of everyday household items from homes around the world that visually captures socioeconomic diversity of traditionally underrepresented populations. It consists of public domain data, licensed for academic, commercial and non-commercial usage, under CC-BY and CC-BY-SA 4.0. The dataset was developed because similar datasets lack socioeconomic metadata and are not representative of global diversity.

This is a subset of the original dataset that can be used for multiclass classification with 10 categories. It is designed to be used in teaching, similar to the widely used, but unlicensed CIFAR-10 dataset.

These are the preprocessing steps that were performed:

1. Only take examples with one imagenet_synonym label
2. Use only examples with the 10 most frequently occuring labels
3. Downscale images to 64 x 64 pixels
4. Split data in train and test
5. Store as numpy array

This is the label mapping:

Checkout https://github.com/carpentries-lab/deep-learning-intro/blob/main/instructors/prepare-dollar-street-data.ipynb" target="_blank" rel="noopener">this notebook to see how the subset was created.

The original dataset was downloaded from https://www.kaggle.com/datasets/mlcommons/the-dollar-street-dataset. See https://mlcommons.org/datasets/dollar-street/ for more information.

Heart disease remains a leading cause of mortality and morbidity worldwide, necessitating the development of accurate and reliable predictive models to facilitate early detection and intervention. While state of the art work has focused on various machine learning approaches for predicting heart disease, but they could not able to achieve remarkable accuracy. In response to this need, we applied nine machine learning algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), gaussian naïve bayes (NB gaussian), adaptive boosting, and linear regression to predict heart disease based on a range of physiological indicators. Our approach involved feature selection techniques to identify the most relevant predictors, aimed at refining the models to enhance both performance and interpretability. The models were trained, incorporating processes such as grid search hyperparameter tuning, and cross-validation to minimize overfitting. Additionally, we have developed a novel voting system with feature selection techniques to advance heart disease classification. Furthermore, we have evaluated the models using key performance metrics including accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (ROC AUC). Among the models, XGBoost demonstrated exceptional performance, achieving 99% accuracy, precision, F1-Score, 98% recall, and 100% ROC AUC. This study offers a promising approach to early heart disease diagnosis and preventive healthcare.

Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Authors:

• Han Xiao, Kashif Rasul and Roland Vollgraf
• https://arxiv.org/abs/1708.07747

Dataset Obtained From: https://github.com/zalandoresearch/fashion-mnist

All images were sized 28x28 in the original dataset

Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits. * Source

Here's an example of how the data looks (each class takes three-rows): https://github.com/zalandoresearch/fashion-mnist/raw/master/doc/img/fashion-mnist-sprite.png" alt="Visualized Fashion MNIST dataset">

Version 1 (original-images_Original-FashionMNIST-Splits):

• Original images, with the original splits for MNIST: train (86% of images - 60,000 images) set and test (14% of images - 10,000 images) set only.
• This version was not trained

Version 3 (original-images_trainSetSplitBy80_20):

• Original, raw images, with the train set split to provide 80% of its images to the training set and 20% of its images to the validation set
• https://blog.roboflow.com/train-test-split/ https://i.imgur.com/angfheJ.png" alt="Train/Valid/Test Split Rebalancing">

Citation:

@online{xiao2017/online,
 author    = {Han Xiao and Kashif Rasul and Roland Vollgraf},
 title    = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms},
 date     = {2017-08-28},
 year     = {2017},
 eprintclass = {cs.LG},
 eprinttype  = {arXiv},
 eprint    = {cs.LG/1708.07747},
}