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.

PASCAL VOC 2007 is a dataset for image recognition. The twenty object classes that have been selected are:

Person: person Animal: bird, cat, cow, dog, horse, sheep Vehicle: aeroplane, bicycle, boat, bus, car, motorbike, train Indoor: bottle, chair, dining table, potted plant, sofa, tv/monitor

The dataset can be used for image classification and object detection tasks.

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/

1. annotations/
   1. pascalvoc/
      • PascalVOC annotation files
      • ...
   2. yolo/
      • YOLO annotation files
      • ...
2. 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.

This dataset is an augmented version of an existing dataset (10.5281/zenodo.13692223), which comprises images annotated according to Actinidia flower gender. The images were acquired in Quinta do Carrazedo (41.32170534304873, -8.661668130688255) with an iPhone Xr with a single camera and a resolution of 640x640 pixels. The dataset includes images of female and male flowers of Actinidia chinensis cv. 'Hayward'.

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

main_directory/

1. annotations/
   1. pascalvoc/
      • PascalVOC annotation files
      • ...
   2. yolo/
      • YOLO annotation files
      • ...
2. images/
   • Image files
   • ...

Each annotation file links to its image by the file name, so if an image is named "TL_Actinidia_Flower_2024_*_type_of_augmentation.jpg", its annotation files are named "TL_Actinidia_Flower_2024_*_type_of_augmentation.xml" (for PascalVOC format) and "TL_Actinidia_Flower_2024_*type_of_augmentation.txt" (for YOLO format) where in the place of the asterisks are numbers.

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 and annotation name.

This dataset consists of images of wrist (with different kind of bands on it).

Introduction Dataset consists of images of wrist captured using mobile phones in real-world scenario. Images were captured under wide variety of lighting conditions, weather, indoor and outdoor. This dataset can be used for Augmented Reality, Mixed Reality, Rakhi Detection, Wrist-watch Detection, Hand-band Detection, etc.

Dataset Features

Captured by 3000+ unique users Rich in diversity Mobile phone view point Various items on the wrist Consists male and female wrists HD Resolution Various lighting conditions Indoor and Outdoor scene

Dataset Features

Classification and detection annotations available Multiple category annotations possible COCO, PASCAL VOC and YOLO formats

To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more.

Note: All the images are manually captured and verified by a large contributor base on DataCluster platform