The Semantic PASCAL-Part dataset

The Semantic PASCAL-Part dataset is the RDF version of the famous PASCAL-Part dataset used for object detection in Computer Vision. Each image is annotated with bounding boxes containing a single object. Couples of bounding boxes are annotated with the part-whole relationship. For example, the bounding box of a car has the part-whole annotation with the bounding boxes of its wheels.

This original release joins Computer Vision with Semantic Web as the objects in the dataset are aligned with concepts from:

• the provided supporting ontology;
• the WordNet database through its synstes;
• the Yago ontology.

The provided Python 3 code (see the GitHub repo) is able to browse the dataset and convert it in RDF knowledge graph format. This new format easily allows the fostering of research in both Semantic Web and Machine Learning fields.

Structure of the semantic PASCAL-Part Dataset

This is the folder structure of the dataset:

• semanticPascalPart: it contains the refined images and annotations (e.g., small specific parts are merged into bigger parts) of the PASCAL-Part dataset in Pascal-voc style.
  • Annotations_set: the test set annotations in .xml format. For further information See the PASCAL VOC format here.
  • Annotations_trainval: the train and validation set annotations in .xml format. For further information See the PASCAL VOC format here.
  • JPEGImages_test: the test set images in .jpg format.
  • JPEGImages_trainval: the train and validation set images in .jpg format.
  • test.txt: the 2416 image filenames in the test set.
  • trainval.txt: the 7687 image filenames in the train and validation set.

The PASCAL-Part Ontology

The PASCAL-Part OWL ontology formalizes, through logical axioms, the part-of relationship between whole objects (22 classes) and their parts (39 classes). The ontology contains 85 logical axiomns in Description Logic in (for example) the following form:

Every potted_plant has exactly 1 plant AND
          has exactly 1 pot

We provide two versions of the ontology: with and without cardinality constraints in order to allow users to experiment with or without them. The WordNet alignment is encoded in the ontology as annotations. We further provide the WordNet_Yago_alignment.csv file with both WordNet and Yago alignments.

The ontology can be browsed with many Semantic Web tools such as:

• Protégé: a graphical tool for ongology modelling;
• OWLAPI: Java API for manipulating OWL ontologies;
• rdflib: Python API for working with the RDF format.
• RDF stores: databases for storing and semantically retrieve RDF triples. See here for some examples.

Citing semantic PASCAL-Part

If you use semantic PASCAL-Part in your research, please use the following BibTeX entry

@article{DBLP:journals/ia/DonadelloS16,
 author  = {Ivan Donadello and
        Luciano Serafini},
 title   = {Integration of numeric and symbolic information for semantic image
        interpretation},
 journal  = {Intelligenza Artificiale},
 volume  = {10},
 number  = {1},
 pages   = {33--47},
 year   = {2016}
}

Pascal VOC 2012 is common benchmark for object detection. It contains common objects that one might find in images on the web.

https://i.imgur.com/y2sB9fD.png" alt="Image example">

Note: the test set is witheld, as is common with benchmark datasets.

You can think of it sort of like a baby COCO.

This dataset contains the data from the PASCAL Visual Object Classes Challenge, corresponding to the Classification and Detection competitions.

In the Classification competition, the goal is to predict the set of labels contained in the image, while in the Detection competition the goal is to predict the bounding box and label of each individual object. WARNING: As per the official dataset, the test set of VOC2012 does not contain annotations.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('voc', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/voc-2007-5.0.0.png" alt="Visualization" width="500px">

Dataset for tree crown detection with Pascal VOC XML format for objection detection.

The images in the dataset ( VOC format) were captured by a UAV at an altitude of 30 meters. The collected images were annotated in PASCAL VOC format. A total of 11,158 instances in 2,440 images are incorporated in the dataset.

• The UAV-PDD2023 dataset, captured by unmanned aerial vehicles (UAVs), provides a benchmark for road damage detection. It is highly useful for municipal authorities and road agencies to conduct low-cost road condition monitoring.
• Six types of road damages are labeled in the dataset: Longitudinal cracks (LC), Transverse cracks (TC), Alligator cracks (AC), Oblique cracks (OC), Repair (RP), and Potholes (PH).
• Researchers can use this dataset as a benchmark to evaluate the performance of different algorithms in addressing similar problems, such as image classification and object detection.

Content

This dataset has the images of objects present in pascal Voc 2012-object detection dataset, grouped into directories according to their shapes. It also includes some negative samples extracted through ROI proposals. The images are organized in tfrecords format for training in TPUs.

Acknowledgement

This dataset is created by extracting objects from https://www.kaggle.com/huanghanchina/pascal-voc-2012. Thanks to Hans .

If you use this dataset, please cite this paper: Puertas, E.; De-Las-Heras, G.; Sánchez-Soriano, J.; Fernández-Andrés, J. Dataset: Variable Message Signal Annotated Images for Object Detection. Data 2022, 7, 41. https://doi.org/10.3390/data7040041

This dataset consists of Spanish road images taken from inside a vehicle, as well as annotations in XML files in PASCAL VOC format that indicate the location of Variable Message Signals within them. Also, a CSV file is attached with information regarding the geographic position, the folder where the image is located, and the text in Spanish. This can be used to train supervised learning computer vision algorithms, such as convolutional neural networks. Throughout this work, the process followed to obtain the dataset, image acquisition, and labeling, and its specifications are detailed. The dataset is constituted of 1216 instances, 888 positives, and 328 negatives, in 1152 jpg images with a resolution of 1280x720 pixels. These are divided into 576 real images and 576 images created from the data-augmentation technique. The purpose of this dataset is to help in road computer vision research since there is not one specifically for VMSs.

The folder structure of the dataset is as follows:

• vms_dataset/
  • data.csv
  • real_images/
    • imgs/
    • annotations/
  • data-augmentation/
    • imgs/
    • annotations/

In which:

• data.csv: Each row contains the following information separated by commas (,): image_name, x_min, y_min, x_max, y_max, class_name, lat, long, folder, text.
• real_images: Images extracted directly from the videos.
• data-augmentation: Images created using data-augmentation
• imgs: Image files in .jpg format.
• annotations: Annotation files in .xml format.

Load PascalVOC dataset Load PascalVOC dataset. This algorithm converts a given dataset in PascalVOC 2012 format to Ikomia format....

This dataset consists of CT and PET-CT DICOM images of lung cancer subjects with XML Annotation files that indicate tumor location with bounding boxes. The images were retrospectively acquired from patients with suspicion of lung cancer, and who underwent standard-of-care lung biopsy and PET/CT. Subjects were grouped according to a tissue histopathological diagnosis. Patients with Names/IDs containing the letter 'A' were diagnosed with Adenocarcinoma, 'B' with Small Cell Carcinoma, 'E' with Large Cell Carcinoma, and 'G' with Squamous Cell Carcinoma.

The images were analyzed on the mediastinum (window width, 350 HU; level, 40 HU) and lung (window width, 1,400 HU; level, –700 HU) settings. The reconstructions were made in 2mm-slice-thick and lung settings. The CT slice interval varies from 0.625 mm to 5 mm. Scanning mode includes plain, contrast and 3D reconstruction.

Before the examination, the patient underwent fasting for at least 6 hours, and the blood glucose of each patient was less than 11 mmol/L. Whole-body emission scans were acquired 60 minutes after the intravenous injection of 18F-FDG (4.44MBq/kg, 0.12mCi/kg), with patients in the supine position in the PET scanner. FDG doses and uptake times were 168.72-468.79MBq (295.8±64.8MBq) and 27-171min (70.4±24.9 minutes), respectively. 18F-FDG with a radiochemical purity of 95% was provided. Patients were allowed to breathe normally during PET and CT acquisitions. Attenuation correction of PET images was performed using CT data with the hybrid segmentation method. Attenuation corrections were performed using a CT protocol (180mAs,120kV,1.0pitch). Each study comprised one CT volume, one PET volume and fused PET and CT images: the CT resolution was 512 × 512 pixels at 1mm × 1mm, the PET resolution was 200 × 200 pixels at 4.07mm × 4.07mm, with a slice thickness and an interslice distance of 1mm. Both volumes were reconstructed with the same number of slices. Three-dimensional (3D) emission and transmission scanning were acquired from the base of the skull to mid femur. The PET images were reconstructed via the TrueX TOF method with a slice thickness of 1mm.

The location of each tumor was annotated by five academic thoracic radiologists with expertise in lung cancer to make this dataset a useful tool and resource for developing algorithms for medical diagnosis. Two of the radiologists had more than 15 years of experience and the others had more than 5 years of experience. After one of the radiologists labeled each subject the other four radiologists performed a verification, resulting in all five radiologists reviewing each annotation file in the dataset. Annotations were captured using Labellmg. The image annotations are saved as XML files in PASCAL VOC format, which can be parsed using the PASCAL Development Toolkit: https://pypi.org/project/pascal-voc-tools/. Python code to visualize the annotation boxes on top of the DICOM images can be downloaded here.

Two deep learning researchers used the images and the corresponding annotation files to train several well-known detection models which resulted in a maximum a posteriori probability (MAP) of around 0.87 on the validation set.

Dataset link: https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=70224216

Context

Original Dataset is used in the paper, (S. Liu, J. Feng, C. Domokos, H. Xu, J. Huang, Z. Hu, & S. Yan. 2014) CFPD | Fashion Parsing with Weak Color-Category Labels, with for Object Detection and Segmentation tasks (https://sites.google.com/site/fashionparsing)

This dataset is custom for Object Detection task, with remove skin, face, background infomation, and format follow PASCAL VOC format. The classes of the this dataset: -sunglass, -hat, -jacket, -shirt, -pants, -shorts, -skirt, -dress, -bag, -shoe

Note: If you want .txt file with YOLO format, you can use Annotations_txt directory.

boulder_detection Dataset

An object detection dataset in YOLO format containing 3 splits: train, val, test.

  Dataset Metadata

License: CC-BY-4.0 (Creative Commons Attribution 4.0 International) Version: 1.0 Date Published: 2025-05-16 Cite As: TBD

  Dataset Details

Format: YOLO

Splits: train, val, test

Classes: boulder

  Additional Formats

Includes COCO format annotations Includes Pascal VOC format annotations

  Data Format

This dataset… See the full description on the dataset page: https://huggingface.co/datasets/gremlin97/boulder_detection.

conequest_detection Dataset

An object detection dataset in YOLO format containing 3 splits: train, val, test.

  Dataset Metadata

License: CC-BY-4.0 (Creative Commons Attribution 4.0 International) Version: 1.0 Date Published: 2025-05-11 Cite As: TBD

  Dataset Details

Format: YOLO

Splits: train, val, test

Classes: cone

  Additional Formats

Includes COCO format annotations Includes Pascal VOC format annotations

  Usage

from datasets import… See the full description on the dataset page: https://huggingface.co/datasets/gremlin97/conequest_detection.

This dataset is an augmented version of an existing dataset (10.5281/zenodo.5213824), composed by visible and thermal images with trunk annotations. The images were acquired in three different portuguese forests and were captured by five different cameras:

GoPro Hero6

Allied Mako G-125

FLIR M232

ZED Stereo

OAK-D

The augmented images and their annotations are stored in an archive with the following structure:

main_directory/

annotations/

pascalvoc/

PascalVOC annotation files

...

yolo/

YOLO annotation files

...

images/

Image files

...

Each annotation file links to its image by the file name, so if an image is named "img12345.jpg", its annotation files are named as "img12345.xml" (for PascalVOC format) and "img12345.txt" (for YOLO format).

The dataset contains original and augmented images. The original images' names follow the pattern "img_******.jpg", where in the place of the asterisks are numbers. The remaining images are the augmented ones.

Also, the subsets that were used to train, validate and test some deep learning models are available in three .TXT files (train.txt, val.txt and test.txt), where each file line corresponds to an image name.

Explore the Cars Drone Detection Dataset featuring high-resolution images (512x512 pixels) with precise car annotations using the Pascal VOC format.

The dataset comprises edited recordings of Mexican sign language's Dactylology (29 signs) and Ten First Numbers (from 1 to 10), including static and continuous signs accordingly from person 1 to person 5. The edited recordings are organized for easy access and management. Edited videos and screenshots of static signs are labeled in their file with corresponding sign language representations and stored in a consistent order per person, the number of the cycle of recording, and per hand. Static sign images can be exported in PASCAL VOC format with XML annotations too. The dataset is designed to facilitate feature extraction and further analysis in Mexican sign language recognition research.

We provide manually annotated bounding boxes of sandhill crane targets in thermal imagery for use in deep learning models. The dataset contains forty files, each file representing the manual annotations created for a single image. We used the open-source tool labelImg (https://pypi.org/project/labelImg/) to create annotations and saved them in PASCAL VOC format.

Name: ZeroCostDL4Mic - YoloV2 example training and test dataset

(see our Wiki for details)

Data type: 2D grayscale .png images with corresponding bounding box annotations in .xml PASCAL Voc format.

Microscopy data type: Phase contrast microscopy data (brightfield)

Microscope: Inverted Zeiss Axio zoom widefield microscope equipped with an AxioCam MRm camera, an EL Plan-Neofluar 20 × /0.5 NA objective (Carl Zeiss), with a heated chamber (37 °C) and a CO2 controller (5%).

Cell type: MDA-MB-231 cells migrating on cell-derived matrices generated by fibroblasts.

File format: .png (8-bit)

Image size: 1388 x 1040 px (323 nm)

Author(s): Guillaume Jacquemet1,2,3, Lucas von Chamier4,5

Contact email: lucas.chamier.13@ucl.ac.uk and guillaume.jacquemet@abo.fi

Affiliation(s):

1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland

2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku

3) ORCID: 0000-0002-9286-920X

4) MRC-Laboratory for Molecular Cell Biology. University College London, London, UK

5) ORCID: 0000-0002-9243-912X

Associated publications: Jacquemet et al 2016. DOI: 10.1038/ncomms13297

Funding bodies: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.

This dataset is a collection of manually and automatically annotated multispectral Images over Raphanus Raphanistrum infestations among Wheat crops.The images are categorized in two directories namely 'Manual' and 'Auotmated'. The sub-directory 'Manual' consists of manually acquired 85 images in .PNG format and annotations in COCO segmentation format titled region_data.json. Whereas, the sub-directory 'Automated' consists of 80 automatically annotated images in .JPG format and 80 annotation files in .XML Pascal VOC format.

The scientific framework of image acquisition and annotations are explained in the Data in Brief paper. This is just a prerequisite to the data article.

Roles:

The image acquisition was performed by Mariano Crimaldi, a researcher, on behalf of Department of Agriculture and the hosting institution University of Naples Federico II, Italy.Shubham Rana has been the curator and analyst for the data under the supervision of his PhD supervisor Prof. Salvatore Gerbino. They are affiliated with Department of Engineering, University of Campania 'Luigi Vanvitelli'. We are also in the process of articulating a data-in-brief article associated with this repository

Domenico Barretta, Department of Engineering has been associated in consulting and brainstorming role particularly with data and annotation management and litmus testing of the datasets.

Context

Just a simple dataset to demonstrate object detection and classification in the retail environment, preferably using computer vision.

Content

This dataset contains resized images which have been annotated using LabelIMG. These resized images are founded in the directory 'ResizedImages' while corresponding XML notations in the Pascal VOC format. I used a YOLOv3 model to use this data. As of November 13, 2020, only three categories of products exists: 'can', 'shampoo', and 'spice.' Images vary in number of objects, with some images sporting only one object of one class, others sporting multiple object of the same class, and lastly, some sporting multiple objects of different classes.

Inspiration

The inspiration from this dataset was the need for a submission to the FLAIRS conference.

Notice: We have currently a paper under double-blind review that introduces this dataset. Therefore, we have anonymized the dataset authorship. Once the review process has concluded, we will update the authorship information of this dataset.

Chinese Chemical Safety Signs (CCSS)

This dataset is compiled as a benchmark for recognizing chemical safety signs from images. We provide both the dataset and the experimental results at doi:10.5281/zenodo.5482334.

1. The Dataset

The complete dataset is contained in the folder ccss/data in archive css_data.zip. The images include signs based on the Chinese standard "Safety Signs and their Application Guidelines" (GB 2894-2008) for safety signs in chemical environments. This standard, in turn, refers to the standards ISO 7010 (Graphical symbols – Safety Colours and Safety Signs – Safety signs used in workplaces and public areas), GB/T 10001 (Public Information Graphic Symbols for Signs), and GB 13495 (Fire Safety Signs)

1.1. Image Collection

We collect photos commonly used chemical safety signs in chemical laboratories and chemical teaching buildings. For a discussion of the standards we base our collections, refer to the book "Talking about Hazardous Chemicals and Safety Signs" for common signs, and refer to the safety signs guidelines (GB 2894-2008).

The shooting was mainly carried out in 6 locations, namely on the road, in a parking lot, construction walls, in a chemical laboratory, outside near big machines, and inside the factory and corridor.

Shooting scale: Images in which the signs appear in small, medium and large scales were taken for each location by shooting photos from different distances.

Shooting light: good lighting conditions and poor lighting conditions were investigated.

Part of the images contain multiple targets and the other part contains only single signs.

Under all conditions, a total of 4650 photos were taken in the original data. These were expanded to 27'900 photos were via data enhancement. All images are located in folder ccss/data/JPEGImages.

The file ccss/data/features/enhanced_data_to_original_data.csv provides a mapping between the enhanced image name and the corresponding original image.

1.2. Annotation and Labelling

The labelling tool is Labelimg, which uses the PASCAL-VOC labelling format. The annotation is stored in the folder ccss/data/Annotations.

Faster R-CNN and SSD are two algorithms that use this format. When training YOLOv5, you can run trans_voc2yolo.py to convert the XML file in PASCAL-VOC format to a txt file.

We provide further meta-information about the dataset in form of a CSV file features.csv which notes, for each image, which other features it has (lighting conditions, scale, multiplicity, etc.).

1.3. Dataset Features

As stated above, the images have been shot under different conditions. We provide all the feature information in folder ccss/data/features. For each feature, there is a separate list of file names in that folder. The file ccss/data/features/features_on_original_data.csv is a CSV file which notes all the features of each original image.

1.4. Dataset Division

The data set is fixedly divided into 7:3 training set and test set. You can find the corresponding image names in the files ccss/data/training_data_file_names.txt and ccss/data/test_data_file_names.txt.

1. Baseline Experiments

We provide baseline results with the three models of Faster R-CNN, SSD, and YOLOv5. All code and results is given in folder ccss/experiment in archive ccss_experiment.

2.2. Environment and Configuration

Single Intel Core i7-8700 CPU

NVIDIA GTX1060 GPU

16 GB of RAM

Python: 3.8.10

pytorch: 1.9.0

pycocotools: pycocotools-win

Visual Studio 2017

Windows 10

2.3. Applied Models

The source codes and results of the applied models is given in folder ccss/experiment with sub-folders corresponding to the model names.

2.3.1. Faster R-CNN

backbone: resnet50+fpn.

we downloaded the pre-training weights from

we modify the type information of the JSON file to match our application.

run train_res50_fpn.py

finally, the weights trained by the training set.

backbone: mobilenetv2

the same training method as resnet50+fpn, but the effect is not as good as resnet50+fpn, so it is directly discarded.

The Faster R-CNN source code used in our experiment is given in folder ccss/experiment/sources/faster_rcnn. The weights of the fully-trained Faster R-CNN model are stored in file ccss/experiment/trained_models/faster_rcnn.pth. The performance measurements of Faster R-CNN are stored in folder ccss/experiment/performance_indicators/faster_rcnn.

2.3.2. SSD

backbone: resnet50

we downloaded pre-training weights from

the same training method as Faster R-CNN is applied.

The SSD source code used in our experiment is given in folder ccss/experiment/sources/ssd. The weights of the fully-trained SSD model are stored in file ccss/experiment/trained_models/ssd.pth. The performance measurements of SSD are stored in folder ccss/experiment/performance_indicators/ssd.

2.3.4. YOLOv5

backbone: CSP_DarkNet

we modified the type information of the YML file to match our application

run trans_voc2yolo.py to convert the XML file in VOC format to a txt file.

the weights used are: yolov5s.

The YOLOv5 source code used in our experiment is given in folder ccss/experiment/sources/yolov5. The weights of the fully-trained YOLOv5 model are stored in file ccss/experiment/trained_models/yolov5.pt. The performance measurements of YOLOv5 are stored in folder ccss/experiment/performance_indicators/yolov5.

2.4. Evaluation

The computed evaluation metrics as well as the code needed to compute them from our dataset are provided in the folder ccss/experiment/performance_indicators. They are provided over the complete test st as well as separately for the image features (over the test set).

1. Code Sources

Faster R-CNN

official code:

SSD

official code:

YOLOv5

We are particularly thankful to the author of the GitHub repository WZMIAOMIAO/deep-learning-for-image-processing (with whom we are not affiliated). Their instructive videos and codes were most helpful during our work. In particular, we based our own experimental codes on his work (and obtained permission to include it in this archive).

1. Licensing

While our dataset and results are published under the Creative Commons Attribution 4.0 License, this does not hold for the included code sources. These sources are under the particular license of the repository where they have been obtained from (see Section 3 above).