Pothole Dataset

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This is a collection of 665 images of roads with the potholes labeled. The dataset was created and shared by Atikur Rahman Chitholian as part of his undergraduate thesis and was originally shared on Kaggle.

Note: The original dataset did not contain a validation set; we have re-shuffled the images into a 70/20/10 train-valid-test split.

Usage

This dataset could be used for automatically finding and categorizing potholes in city streets so the worst ones can be fixed faster.

The dataset is provided in a wide variety of formats for various common machine learning models.

Antiviral peptides (AVPs) are bioactive peptides that exhibit the inhibitory activity against viruses through a range of mechanisms. Virus entry inhibitory peptides (VEIPs) make up a specific class of AVPs that can prevent envelope viruses from entering cells. With the growing number of experimentally verified VEIPs, there is an opportunity to use machine learning to predict peptides that inhibit the virus entry. In this paper, we have developed the first target-specific prediction model for the identification of new VEIPs using, along with the peptide sequence characteristics, the attributes of the envelope proteins of the target virus, which overcomes the problem of insufficient data for particular viral strains and improves the predictive ability. The model’s performance was evaluated through 10 repeats of 10-fold cross-validation on the training data set, and the results indicate that it can predict VEIPs with 87.33% accuracy and Matthews correlation coefficient (MCC) value of 0.76. The model also performs well on an independent test set with 90.91% accuracy and MCC of 0.81. We have also developed an automatic computational tool that predicts VEIPs, which is freely available at https://dbaasp.org/tools?page=linear-amp-prediction.

This data set presents a corpus of light-weight files designed to test the validation criteria of JHOVE's PDF module against "well-formedness". Test cases are based on structural requirements for PDF files as per ISO 32000-1:2008 standard. The basis for all test files is a single page, one line document with no special features such as linearization. While such a light-weight document only allows to check against a fragment of standard requirements, the focus was put on basic structure violations at the header, trailer, document catalog, page tree node and cross-reference levels. The test set also checks for basic violations at the page node, page resource and stream object level. The accompanying spreadsheet briefly categorizes and describes the test set and includes the outcome when running the test set against JHOVE 1.16, PDF-hul 1.8 as well as Adobe Acrobat Professional XI Pro (11.0.15). The spreadsheet also includes a codecov coverage statistic for the test set in relation to the JHOVE 1.16, PDF-hul 1.8 module. Further information can be found in the paper "A PDF Test-Set for Well-Formedness Validation in JHOVE - The Good, the Bad and the Ugly", published in the proceedings of the 14th International Conference on Digital Preservation (Kyoto, Japan, September 25-29 2017). While the spreadsheet only contains results of running the test set against JHOVE, it can be used as a ground truth for any file format validation process.

Bats play crucial ecological roles and provide valuable ecosystem services, yet many populations face serious threats from various ecological disturbances. The North American Bat Monitoring Program (NABat) aims to assess status and trends of bat populations while developing innovative and community-driven conservation solutions using its unique data and technology infrastructure. To support scalability and transparency in the NABat acoustic data pipeline, we developed a fully-automated machine-learning algorithm. This dataset includes audio files of bat echolocation calls that were considered to develop V1.0 of the NABat machine-learning algorithm, however the test set (i.e., holdout dataset) has been excluded from this release. These recordings were collected by various bat monitoring partners across North America using ultrasonic acoustic recorders for stationary acoustic and mobile acoustic surveys. For more information on how these surveys may be conducted, see Chapters 4 and 5 of “A Plan for the North American Bat Monitoring Program” (https://doi.org/10.2737/SRS-GTR-208). These data were then post-processed by bat monitoring partners to remove noise files (or those that do not contain recognizable bat calls) and apply a species label to each file. There is undoubtedly variation in the steps that monitoring partners take to apply a species label, but the steps documented in “A Guide to Processing Bat Acoustic Data for the North American Bat Monitoring Program” (https://doi.org/10.3133/ofr20181068) include first processing with an automated classifier and then manually reviewing to confirm or downgrade the suggested species label. Once a manual ID label was applied, audio files of bat acoustic recordings were submitted to the NABat database in Waveform Audio File format. From these available files in the NABat database, we considered files from 35 classes (34 species and a noise class). Files for 4 species were excluded due to low sample size (Corynorhinus rafinesquii, N=3; Eumops floridanus, N =3; Lasiurus xanthinus, N = 4; Nyctinomops femorosaccus, N =11). From this pool, files were randomly selected until files for each species/grid cell combination were exhausted or the number of recordings reach 1250. The dataset was then randomly split into training, validation, and test sets (i.e., holdout dataset). This data release includes all files considered for training and validation, including files that had been excluded from model development and testing due to low sample size for a given species or because the threshold for species/grid cell combinations had been met. The test set (i.e., holdout dataset) is not included. Audio files are grouped by species, as indicated by the four-letter species code in the name of each folder. Definitions for each four-letter code, including Family, Genus, Species, and Common name, are also included as a dataset in this release.

Dataset Description

Context and Methodology

• Research Domain/Project:
  This dataset was created for a machine learning experiment aimed at developing a classification model to predict outcomes based on a set of features. The primary research domain is disease prediction in patients. The dataset was used in the context of training, validating, and testing.

• Purpose of the Dataset:
  The purpose of this dataset is to provide training, validation, and testing data for the development of machine learning models. It includes labeled examples that help train classifiers to recognize patterns in the data and make predictions.

• Dataset Creation:
  Data preprocessing steps involved cleaning, normalization, and splitting the data into training, validation, and test sets. The data was carefully curated to ensure its quality and relevance to the problem at hand. For any missing values or outliers, appropriate handling techniques were applied (e.g., imputation, removal, etc.).

Technical Details

• Structure of the Dataset:
  The dataset consists of several files organized into folders by data type:

  • Training Data: Contains the training dataset used to train the machine learning model.

  • Validation Data: Used for hyperparameter tuning and model selection.

  • Test Data: Reserved for final model evaluation.

  Each folder contains files with consistent naming conventions for easy navigation, such as train_data.csv, validation_data.csv, and test_data.csv. Each file follows a tabular format with columns representing features and rows representing individual data points.

• Software Requirements:
  To open and work with this dataset, you need VS Code or Jupyter, which could include tools like:

  • Python (with libraries such as pandas, numpy, scikit-learn, matplotlib, etc.)

Further Details

• Reusability:
  Users of this dataset should be aware that it is designed for machine learning experiments involving classification tasks. The dataset is already split into training, validation, and test subsets. Any model trained with this dataset should be evaluated using the test set to ensure proper validation.

• Limitations:
  The dataset may not cover all edge cases, and it might have biases depending on the selection of data sources. It's important to consider these limitations when generalizing model results to real-world applications.

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.

Context

Please upvote if you find this dataset of use. - Thank you This version is an update of the earlier version. I ran a data set quality evaluation program on the previous version which found a considerable number of duplicate and near duplicate images. Duplicate images can lead to falsely higher values of validation and test set accuracy and I have eliminated these images in this version of the dataset. Images were gathered from internet searches. The images were scanned with a duplicate image detector program I wrote. Any duplicate images were removed to prevent bleed through of images between the train, test and valid data sets. All images were then resized to 224 X224 X 3 and converted to jpg format. A csv file is included that for each image file contains the relative path to the image file, the image file class label and the dataset (train, test or valid) that the image file resides in. This is a clean dataset. If you build a good model you should achieve at least 95% accuracy on the test set. If you build a very good model for example using transfer learning you should be able to achieve 98%+ on test set accuracy. If you find this data set useful please upvote. Thanks

Content

Collection of sports images covering 100 different sports.. Images are 224,224,3 jpg format. Data is separated into train, test and valid directories. Additionallly a csv file is included for those that wish to use it to create there own train, test and validation datasets. .

Inspiration

Wanted to build a high quality clean data set that was easy to use and had no bad images or duplication between the train, test and validation data sets. Provides a good data set to test your models on. Design for straight forward application of keras preprocessing functions like ImageDataenerator.flow_from_directory or if you use the csv file ImageDataGenerator.flow_from_dataframe. This dataset was carefully created so that the region of interest (ROI) in this case the sport occupies approximately 50% of the pixels in the image. As a consequence even models of moderate complexity should achieve training and validation accuracies in the high 90's.

This repository accompanies the manuscript "Spatially resolved uncertainties for machine learning potentials" by E. Heid, J. Schörghuber, R. Wanzenböck, and G. K. H. Madsen. The following files are available:

• mc_experiment.ipynb is a Jupyter notebook for the Monte Carlo experiment described in the study (artificial model with only variance as error source).

• aggregate_cut_relax.py contains code to cut and relax boxes for the water active learning cycle.

• data_t1x.tar.gz contains reaction pathways for 10,073 reactions from a subset of the Transition1x dataset, split into training, validation and test sets. The training and validation sets contain the indices 1, 2, 9, and 10 from a 10-image nudged-elastic band search (40k datapoints), while the test set contains indices 3-8 (60k datapoints). The test set is ordered according to the reaction and index, i.e. rxn1_index3, rxn1_index4, [...] rxn1_index8, rxn2_index3, [...].

• data_sto.tar.gz contains surface reconstructions of SrTiO3, randomly split into a training and validation set, as well as a test set.

• data_h2o.tar.gz contains:

  • full_db.extxyz: The full dataset of 1.5k structures.

  • iter00_train.extxyz and iter00_validation.extxyz: The initial training and validation set for the active learning cycle.

  • the subfolders in the folders random, and uncertain, and atomic contain the training and validation sets for the random and uncertainty-based (local or atomic) active learning loops.

Context

I do a lot of work with image data sets. Often it is necessary to partition the images into male and female data sets. Doing this by hand can be a long and tedious task particularly on large data sets. So I decided to create a classifier that could do the task for me.

Content

I used the CELEBA aligned data set to provide the images. I went through and separated the images visually into 1747 female and 1747 male training images. I also created 100 male and 100 female test image and 100 male, 100 female validation images. I want to only the face to be in the image so I developed an image cropping function using MTCNN to crop all the images. That function is included as one of the notebooks should anyone have a need for a good face cropping function. I also created an image duplicate detector to try to eliminate any of the training images from appearing in the test or validation images. I have developed a general purpose image classification function that works very well for most image classification tasks. It contains the option to select 1 of 7 models for use. For this application I used the MobileNet model because it is less computationally expensive and gives excellent results. On the test set accuracy is near 100%.

Acknowledgements

The CELEBA aligned data set was used. This data set is very large and of good quality. To crop the images to only include the face I developed a face cropping function using MTCNN. MTCNN is a very accurate program and is reasonably fast, however it is notflawless so after cropping the iages you shouldalways visually inspect the results.

Inspiration

I developed this data set to train a classifier to be able to distinguish the gender shown in an image. Why bother you may ask I can just look at the image and tell. True but lets say you have a data set of 50,000 images that you want to separate it into male and female data sets. Doing that by hand would take forever. With the trained classifier with near 100% accuracy you can use the classifier with model.predict to do the job for you.

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 via 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.

This is the dataset for the Style Change Detection task of PAN 2022. Task The goal of the style change detection task is to identify text positions within a given multi-author document at which the author switches. Hence, a fundamental question is the following: If multiple authors have written a text together, can we find evidence for this fact; i.e., do we have a means to detect variations in the writing style? Answering this question belongs to the most difficult and most interesting challenges in author identification: Style change detection is the only means to detect plagiarism in a document if no comparison texts are given; likewise, style change detection can help to uncover gift authorships, to verify a claimed authorship, or to develop new technology for writing support. Previous editions of the Style Change Detection task aim at e.g., detecting whether a document is single- or multi-authored (2018), the actual number of authors within a document (2019), whether there was a style change between two consecutive paragraphs (2020, 2021) and where the actual style changes were located (2021). Based on the progress made towards this goal in previous years, we again extend the set of challenges to likewise entice novices and experts: Given a document, we ask participants to solve the following three tasks: [Task1] Style Change Basic: for a text written by two authors that contains a single style change only, find the position of this change (i.e., cut the text into the two authors��� texts on the paragraph-level), [Task2] Style Change Advanced: for a text written by two or more authors, find all positions of writing style change (i.e., assign all paragraphs of the text uniquely to some author out of the number of authors assumed for the multi-author document) [Task3] Style Change Real-World: for a text written by two or more authors, find all positions of writing style change, where style changes now not only occur between paragraphs, but at the sentence level. All documents are provided in English and may contain an arbitrary number of style changes, resulting from at most five different authors. Data To develop and then test your algorithms, three datasets including ground truth information are provided (dataset1 for task 1, dataset2 for task 2, and dataset3 for task 3). Each dataset is split into three parts: training set: Contains 70% of the whole dataset and includes ground truth data. Use this set to develop and train your models. validation set: Contains 15% of the whole dataset and includes ground truth data. Use this set to evaluate and optimize your models. test set: Contains 15% of the whole dataset, no ground truth data is given. This set is used for evaluation (see later). You are free to use additional external data for training your models. However, we ask you to make the additional data utilized freely available under a suitable license. Input Format The datasets are based on user posts from various sites of the StackExchange network, covering different topics. We refer to each input problem (i.e., the document for which to detect style changes) by an ID, which is subsequently also used to identify the submitted solution to this input problem. We provide one folder for train, validation, and test data for each dataset, respectively. For each problem instance X (i.e., each input document), two files are provided: problem-X.txt contains the actual text, where paragraphs are denoted by for tasks 1 and 2. For task 3, we provide one sentence per paragraph (again, split by ). truth-problem-X.json contains the ground truth, i.e., the correct solution in JSON format. An example file is listed in the following (note that we list keys for the three tasks here): { "authors": NUMBER_OF_AUTHORS, "site": SOURCE_SITE, "changes": RESULT_ARRAY_TASK1 or RESULT_ARRAY_TASK3, "paragraph-authors": RESULT_ARRAY_TASK2 } The result for task 1 (key "changes") is represented as an array, holding a binary for each pair of consecutive paragraphs within the document (0 if there was no style change, 1 if there was a style change). For task 2 (key "paragraph-authors"), the result is the order of authors contained in the document (e.g., [1, 2, 1] for a two-author document), where the first author is "1", the second author appearing in the document is referred to as "2", etc. Furthermore, we provide the total number of authors and the Stackoverflow site the texts were extracted from (i.e., topic). The result for task 3 (key "changes") is similarly structured as the results array for task 1. However, for task 3, the changes array holds a binary for each pair of consecutive sentences and they may be multiple style changes in the document. An example of a multi-author document with a style change between the third and fourth paragraph (or sentence for task 3) could be described as follows (we only list the relevant key/value pairs here): { "changes": [0,0,1,...], "paragraph-authors": [1,1,1,2,...] } Output Format To...

Introduction The data set is based on 3,004 images collected by the Pancam instruments mounted on the Opportunity and Spirit rovers from NASA's Mars Exploration Rovers (MER) mission. We used rotation, skewing, and shearing augmentation methods to increase the total collection to 70,864 (see Image Augmentation section for more information). Based on the MER Data Catalog User Survey [1], we identified 25 classes of both scientific (e.g. soil trench, float rocks, etc.) and engineering (e.g. rover deck, Pancam calibration target, etc.) interests (see Classes section for more information). The 3,004 images were labeled on Zooniverse platform, and each image is allowed to be assigned with multiple labels. The images are either 512 x 512 or 1024 x 1024 pixels in size (see Image Sampling section for more information). Classes There is a total of 25 classes for this data set. See the list below for class names, counts, and percentages (the percentages are computed as count divided by 3,004). Note that the total counts don't sum up to 3,004 and the percentages don't sum up to 1.0 because each image may be assigned with more than one class. Class name, count, percentage of dataset Rover Deck, 222, 7.39% Pancam Calibration Target, 14, 0.47% Arm Hardware, 4, 0.13% Other Hardware, 116, 3.86% Rover Tracks, 301, 10.02% Soil Trench, 34, 1.13% RAT Brushed Target, 17, 0.57% RAT Hole, 30, 1.00% Rock Outcrop, 1915, 63.75% Float Rocks, 860, 28.63% Clasts, 1676, 55.79% Rocks (misc), 249, 8.29% Bright Soil, 122, 4.06% Dunes/Ripples, 1000, 33.29% Rock (Linear Features), 943, 31.39% Rock (Round Features), 219, 7.29% Soil, 2891, 96.24% Astronomy, 12, 0.40% Spherules, 868, 28.89% Distant Vista, 903, 30.23% Sky, 954, 31.76% Close-up Rock, 23, 0.77% Nearby Surface, 2006, 66.78% Rover Parts, 301, 10.02% Artifacts, 28, 0.93% Image Sampling Images in the MER rover Pancam archive are of sizes ranging from 64x64 to 1024x1024 pixels. The largest size, 1024x1024, was by far the most common size in the archive. For the deep learning dataset, we elected to sample only 1024x1024 and 512x512 images as the higher resolution would be beneficial to feature extraction. In order to ensure that the data set is representative of the total image archive of 4.3 million images, we elected to sample via "site code". Each Pancam image has a corresponding two-digit alphanumeric "site code" which is used to track location throughout its mission. Since each "site code" corresponds to a different general location, sampling a fixed proportion of images taken from each site ensure that the data set contained some images from each location. In this way, we could ensure that a model performing well on this dataset would generalize well to the unlabeled archive data as a whole. We randomly sampled 20% of the images at each site within the subset of Pancam data fitting all other image criteria, applying a floor function to non-whole number sample sizes, resulting in a dataset of 3,004 images. Train/validation/test sets split The 3,004 images were split into train, validation, and test data sets. The split was done so that roughly 60, 15, and 25 percent of the 3,004 images would end up as train, validation, and test data sets respectively, while ensuing that images from a given site are not split between train/validaiton/test data sets. This resulted in 1,806 train images, 456 validation images, and 742 test images. Augmentation To augment the images in train and validation data sets (note that images in the test data set were not augmented), three augmentation methods were chosen that best represent transformations that could be realistically seen in Pancam images. The three augmentations methods are rotation, skew, and shear. The augmentation methods were applied with random magnitude, followed by a random horizontal flipping, to create 30 augmented images for each image. Since each transformation is followed by a square crop in order to keep input shape consistent, we had to constrict the magnitude limits of each augmentation to avoid cropping out important features at the edges of input images. Thus, rotations were limited to 15 degrees in either direction, the 3-dimensional skew was limited to 45 degrees in any direction, and shearing was limited to 10 degrees in either direction. Note that augmentation was done only on training and validation images. Directory Contents images: contains all 70,864 images train-set-v1.1.0.txt: label file for the training data set val-set-v1.1.0.txt: label file for the validation data set test-set-v1.1.0.txt: label file for the testing data set Images with relatively short file names (e.g., 1p128287181mrd0000p2303l2m1.img.jpg) are original images, and images with long file names (e.g., 1p128287181mrd0000p2303l2m1.img.jpg_04140167-5781-49bd-a913-6d4d0a61dab1.jpg) are augmented images. The label files are formatted as "Image name, Class1, Class2, ..., ClassN". Reference [1] S.B. Cole, J.C. Aubele, B.A. Cohen, S.M. Milkovich, and S.A...

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.

LandCoverPT - A dataset for training Machine Learning models to classify the Portuguese territory land cover. The creation of the dataset used 26 Sentinel-2 products, captured in June and August 2019, and the products where divided into 153347 patches with 120×120 pixels each. The products cover the Portuguese mainland. The Sentinel-2 data was complemented with Corine Land Cover 2018 data. Each patch includes B01, B02, B03, B04, B05, B06, B07, B08, B8A, B09, B11, and B12 Sentinel-2 bands, and the CLC 2018 layer. The dataset is stored in 3 TFRecord files, for training, validation, and test. The training set contains 98141 patches, the validation set includes 24536 patches, and the test set contains 30670 patches. It is also provided code files to experiment with the dataset: - two configuration files in JSON format - a python file with the U-Net model - a python file with a class to create a tf.data.Dataset based on the TFRecord files - a notebook for training the U-Net model on the LandCoverPT dataset - a notebook for evaluating the U-Net model trained on the LandCoverPT dataset - a notebook to make predictions with the U-Net model trained on the LandCoverPT dataset.

Fruits-360 dataset: A dataset of images containing fruits, vegetables, nuts and seeds

Version: 2025.06.07.0

Content

The following fruits, vegetables and nuts and are included: Apples (different varieties: Crimson Snow, Golden, Golden-Red, Granny Smith, Pink Lady, Red, Red Delicious), Apricot, Avocado, Avocado ripe, Banana (Yellow, Red, Lady Finger), Beans, Beetroot Red, Blackberry, Blueberry, Cabbage, Caju seed, Cactus fruit, Cantaloupe (2 varieties), Carambula, Carrot, Cauliflower, Cherimoya, Cherry (different varieties, Rainier), Cherry Wax (Yellow, Red, Black), Chestnut, Clementine, Cocos, Corn (with husk), Cucumber (ripened, regular), Dates, Eggplant, Fig, Ginger Root, Goosberry, Granadilla, Grape (Blue, Pink, White (different varieties)), Grapefruit (Pink, White), Guava, Hazelnut, Huckleberry, Kiwi, Kaki, Kohlrabi, Kumsquats, Lemon (normal, Meyer), Lime, Lychee, Mandarine, Mango (Green, Red), Mangostan, Maracuja, Melon Piel de Sapo, Mulberry, Nectarine (Regular, Flat), Nut (Forest, Pecan), Onion (Red, White), Orange, Papaya, Passion fruit, Peach (different varieties), Pepino, Pear (different varieties, Abate, Forelle, Kaiser, Monster, Red, Stone, Williams), Pepper (Red, Green, Orange, Yellow), Physalis (normal, with Husk), Pineapple (normal, Mini), Pistachio, Pitahaya Red, Plum (different varieties), Pomegranate, Pomelo Sweetie, Potato (Red, Sweet, White), Quince, Rambutan, Raspberry, Redcurrant, Salak, Strawberry (normal, Wedge), Tamarillo, Tangelo, Tomato (different varieties, Maroon, Cherry Red, Yellow, not ripened, Heart), Walnut, Watermelon, Zucchini (green and dark).

Branches

The dataset has 5 major branches:

-The 100x100 branch, where all images have 100x100 pixels. See _fruits-360_100x100_ folder.

-The original-size branch, where all images are at their original (captured) size. See _fruits-360_original-size_ folder.

-The meta branch, which contains additional information about the objects in the Fruits-360 dataset. See _fruits-360_dataset_meta_ folder.

-The multi branch, which contains images with multiple fruits, vegetables, nuts and seeds. These images are not labeled. See _fruits-360_multi_ folder.

-The _3_body_problem_ branch where the Training and Test folders contain different (varieties of) the 3 fruits and vegetables (Apples, Cherries and Tomatoes). See _fruits-360_3-body-problem_ folder.

How to cite

Mihai Oltean, Fruits-360 dataset, 2017-

Dataset properties

For the 100x100 branch

Total number of images: 138704.

Training set size: 103993 images.

Test set size: 34711 images.

Number of classes: 206 (fruits, vegetables, nuts and seeds).

Image size: 100x100 pixels.

For the original-size branch

Total number of images: 58363.

Training set size: 29222 images.

Validation set size: 14614 images

Test set size: 14527 images.

Number of classes: 90 (fruits, vegetables, nuts and seeds).

Image size: various (original, captured, size) pixels.

For the 3-body-problem branch

Total number of images: 47033.

Training set size: 34800 images.

Test set size: 12233 images.

Number of classes: 3 (Apples, Cherries, Tomatoes).

Number of varieties: Apples = 29; Cherries = 12; Tomatoes = 19.

Image size: 100x100 pixels.

For the meta branch

Number of classes: 26 (fruits, vegetables, nuts and seeds).

For the multi branch

Number of images: 150.

Filename format:

For the 100x100 branch

image_index_100.jpg (e.g. 31_100.jpg) or

r_image_index_100.jpg (e.g. r_31_100.jpg) or

r?_image_index_100.jpg (e.g. r2_31_100.jpg)

where "r" stands for rotated fruit. "r2" means that the fruit was rotated around the 3rd axis. "100" comes from image size (100x100 pixels).

Different varieties of the same fruit (apple, for instance) are stored as belonging to different classes.

For the original-size branch

r?_image_index.jpg (e.g. r2_31.jpg)

where "r" stands for rotated fruit. "r2" means that the fruit was rotated around the 3rd axis.

The name of the image files in the new version does NOT contain the "_100" suffix anymore. This will help you to make the distinction between the original-size branch and the 100x100 branch.

For the multi branch

The file's name is the concatenation of the names of the fruits inside that picture.

Alternate download

The Fruits-360 dataset can be downloaded from:

Kaggle https://www.kaggle.com/moltean/fruits

GitHub https://github.com/fruits-360

How fruits were filmed

Fruits and vegetables were planted in the shaft of a low-speed motor (3 rpm) and a short movie of 20 seconds was recorded.

A Logitech C920 camera was used for filming the fruits. This is one of the best webcams available.

Behind the fruits, we placed a white sheet of paper as a background.

Here i...

Description: Downsized (256x256) camera trap images used for the analyses in "Can CNN-based species classification generalise across variation in habitat within a camera trap survey?", and the dataset composition for each analysis. Note that images tagged as 'human' have been removed from this dataset. Full-size images for the BorneoCam dataset will be made available at LILA.science. The full SAFE camera trap dataset metadata is available at DOI: 10.5281/zenodo.6627707.Project: This dataset was collected as part of the following SAFE research project: Machine learning and image recognition to monitor spatio-temporal changes in the behaviour and dynamics of species interactionsFunding: These data were collected as part of research funded by: NERC (NERC QMEE CDT Studentship, NE/P012345/1, http://gotw.nerc.ac.uk/list_full.asp?pcode=NE%2FP012345%2F1&cookieConsent=A)This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.XML metadata: GEMINI compliant metadata for this dataset is available hereFiles: This dataset consists of 3 files: CT_image_data_info2.xlsx, DN_256x256_image_files.zip, DN_generalisability_code.zipCT_image_data_info2.xlsxThis file contains dataset metadata and 1 data tables:Dataset Images (described in worksheet Dataset_images)Description: This worksheet details the composition of each dataset used in the analysesNumber of fields: 69Number of data rows: 270287Fields: filename: Root ID (Field type: id)camera_trap_site: Site ID for the camera trap location (Field type: location)taxon: Taxon recorded by camera trap (Field type: taxa)dist_level: Level of disturbance at site (Field type: ordered categorical)baseline: Label as to whether image is included in the baseline training, validation (val) or test set, or not included (NA) (Field type: categorical)increased_cap: Label as to whether image is included in the 'increased cap' training, validation (val) or test set, or not included (NA) (Field type: categorical)dist_individ_event_level: Label as to whether image is included in the 'individual disturbance level datasets split at event level' training, validation (val) or test set, or not included (NA) (Field type: categorical)dist_combined_event_level_1: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 1' training or test set, or not included (NA) (Field type: categorical)dist_combined_event_level_2: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 2' training or test set, or not included (NA) (Field type: categorical)dist_combined_event_level_3: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 3' training or test set, or not included (NA) (Field type: categorical)dist_combined_event_level_4: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 4' training or test set, or not included (NA) (Field type: categorical)dist_combined_event_level_5: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 5' training or test set, or not included (NA) (Field type: categorical)dist_combined_event_level_pair_1_2: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1 and 2 (pair)' training set, or not included (NA) (Field type: categorical)dist_combined_event_level_pair_1_3: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1 and 3 (pair)' training set, or not included (NA) (Field type: categorical)dist_combined_event_level_pair_1_4: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1 and 4 (pair)' training set, or not included (NA) (Field type: categorical)dist_combined_event_level_pair_1_5: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1 and 5 (pair)' training set, or not included (NA) (Field type: categorical)dist_combined_event_level_pair_2_3: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2 and 3 (pair)' training set, or not included (NA) (Field type: categorical)dist_combined_event_level_pair_2_4: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2 and 4 (pair)' training set, or not included (NA) (Field type: categorical)dist_combined_event_level_pair_2_5: Label as to whether image is included in the 'disturbance level combination ana...

AG-Data is a comprehensive agricultural image classification dataset comprising 30,797 images across 46 distinct categories. We have split the dataset into a training set, a validation set, and a test set in a 6:2:2 ratio.The following is a detailed description of the AG-data dataset：1. The Sorghum dataset contains 3 categories: BroadLeafWeed (1441 images), class0_sorghum (1404 images), and class1_Grass (1467 images), with a total of 4312 images.2. The Banana dataset comprises 4 categories: cordana (400 images), pestalotiopsis (400 images), sigatoka (400 images), and healthy (400 images), totaling 1600 images.3. The sunflower dataset includes 4 categories: Downy mildew (470 images), Fresh leaf (515 images), Gray mold (398 images), and leaf scars (509 images), with a total of 1892 images.4. The mulberry dataset encompasses 10 categories, each with the following number of images: BlackAustralia (637), BlackOodTurkey (500), Buriram60 (345), ChiangMai60 (500), ChiangMaiBuriram60 (761), Kamphaengsaeng42 (500), RedKing (350), TaiwanMeacha (640), Taiwanstraberry (488), and WhiteKing (541), totaling 4497 images.5. The pomegranate dataset consists of 5 categories: Alternaria (886 images), Anthracnose (1166 images), Bacterial_Blight (966 images), Cercospora (631 images), and healthy (1450 images), with a total of 5099 images.6. The potatoleaf dataset has 7 categories: Bacteria (569 images), Fungi (748 images), Nematode (68 images), pest (611 images), Phytopthora (347 images), Virus (532 images), and healthy (201 images), totaling 3076 images.7. The RicePest dataset includes 10 categories, each with the following number of images: asiatic rice borer (498), brown plant hopper (346), paddy stem maggot (89), rice gall midge (217), rice leaf caterpillar (153), rice leaf roller (716), rice leaf hopper (244), rice water weevil (414), samll brown plant hopper (243), and yellow rice borer (236), totaling 3156 images.8. The cucumber dataset comprises 8 categories, each with 800 images, totaling 6400 images. The categories are: Anthracnose, Bacterial wilt, Belly Rot, Downy mildew, Fresh cucumber, Fresh leaf, Gummy Stem Blight, and Pythium Fruit Rot.

Dataset for Journal recommendation, includes title, abstract, keywords, and journal.

We extracted the journals and more information of:

Jiasheng Sheng. (2022). PubMed-OA-Extraction-dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6330817.

Dataset Components:

data_pubmed_all: This dataset encompasses all articles, each containing the following columns: 'pubmed_id', 'title', 'keywords', 'journal', 'abstract', 'conclusions', 'methods', 'results', 'copyrights', 'doi', 'publication_date', 'authors', 'AKE_pubmed_id', 'AKE_pubmed_title', 'AKE_abstract', 'AKE_keywords', 'File_Name'.

data_pubmed: To focus on recent and relevant publications, we have filtered this dataset to include articles published within the last five years, from January 1, 2018, to December 13, 2022—the latest date in the dataset. Additionally, we have exclusively retained journals with more than 200 published articles, resulting in 262,870 articles from 469 different journals.

data_pubmed_train, data_pubmed_val, and data_pubmed_test: For machine learning and model development purposes, we have partitioned the 'data_pubmed' dataset into three subsets—training, validation, and test—using a random 60/20/20 split ratio. Notably, this division was performed on a per-journal basis, ensuring that each journal's articles are proportionally represented in the training (60%), validation (20%), and test (20%) sets. The resulting partitions consist of 157,540 articles in the training set, 52,571 articles in the validation set, and 52,759 articles in the test set.

Overview

Via Laurence Maroney:

Rock Paper Scissors contains images from a variety of different hands, from different races, ages and genders, posed into Rock / Paper or Scissors and labelled as such. You can download the training set here, and the test set here. These images have all been generated using CGI techniques as an experiment in determining if a CGI-based dataset can be used for classification against real images. I also generated a few images that you can use for predictions. You can find them here.

Note that all of this data is posed against a white background.

Each image is 300×300 pixels in 24-bit color.

There are 2520 examples examples in the trianing set, 840 per class. The validation set contains 372 examples (124 per class). The test set contains 9 unlabeled images per class. (Note: in the source, Laurence calls "validation" as the "test," and "test" the "validation.")

Examples

https://i2.wp.com/www.laurencemoroney.com/wp-content/uploads/2019/02/rock06ck02-085.png?w=300" alt="Rock">

https://i0.wp.com/www.laurencemoroney.com/wp-content/uploads/2019/02/testpaper01-00.png?w=300" alt="Paper">

https://i1.wp.com/www.laurencemoroney.com/wp-content/uploads/2019/02/scissors04-080.png?w=300" alt="Scissors">

Source

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},
}