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Pascal VOC 2007 Dataset

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Pascal VOC 2007 Dataset

What is Pascal VOC 2007 Dataset?

The purpose of the Pascal VOC 2007(PASCAL Visual Object Classes) dataset is to recognize objects in realistic scenarios from a variety of visual object types that are not pre-segmented objects and is basically used for the supervised learning task. The dataset can be used for different challenges such as classification, detection, segmentation, and person layout. There are a whole total of twenty object classes chosen. There are 9,963 photos in all, with 24,640 labeled samples. The data has been divided into 50% for training/validation and 50% for testing. Across the training/validation and test sets, the distributions of pictures and objects per class are roughly equal.

Download Pascal VOC 2007 Dataset in Python

Instead of downloading the Pascal VOC 2007 dataset in Python, you can effortlessly load it in Python via our Deep Lake open-source with just one line of code.

Load Pascal VOC 2007 Dataset Training and validation Subset in Python 

				
					import deeplake
ds = deeplake.load('hub://activeloop/pascal-voc-2007-train-val')

Load Pascal VOC 2007 Dataset Test Subset in Python 

				
					import deeplake
ds = deeplake.load('hub://activeloop/pascal-voc-2007-test')

Pascal VOC 2007 Dataset Structure

Pascal VOC 2007 Data Fields
For train and validation set
  • images: tensor containing the images.
  • instances/instance__mask: tensor for object segmentation.
  • instances/mask_pixel: tensor containing pixel values of instances of objects.
  • boxes/box: tensor that contains the coordinate values of the bounding boxes.
  • boxes/label: tensor that contains the labels of the bounding boxes.
  • boxes/pose: tensor containing the numerical label that represents the index of one of the 5 pose categories.
  • boxes/trunc: tensor containing the numerical label that represents the index if the image is truncated.
  • boxes/difficult: tensor containing the numerical label that represents the index if the image is difficult to annotate.
  • parts/box: tensor that contains the coordinates of the bounding boxes for body parts used for the person layout challenge.
  • parts/label: tensor that contains the labels of the bounding boxes for body parts used for the person layout challenge.
  • semantics/mask: tensor that represents the class mask.
  • semantics/label: tensor that contains the labels of classes present in the image and two extra labels, ‘background’ representing the background of the objects and ‘None’ representing the border/outline of the object or objects difficult to label.
  • metadata/segmentation_train_val_split: if the image is a train split tensor containing the text ‘train’. For images used for validation contains the text ‘validation’. If the image is not used for segmentation it contains the text ‘image not used in segmentation’.
  • metadata/part_train_val_split: tensor that contains the text that tells whether the image is used for training or validation or not used in body part or person layout detection.
  • metadata/main_train_val_split: tensor that contains the text that tells if the image is used for training or validation not used in the main set.
  • metadata/image_meta: tensor containing image metadata.
For test set
  • images: tensor containing the images.
  • instance_masks: tensor that represents the instance/object mask based on the number of objects present in the image.
  • boxes/box: tensor that contains the coordinate values of the bounding boxes.
  • boxes/label: tensor that contains the labels of the bounding boxes.
  • boxes/pose: tensor containing the numerical label that represents the index of one of the 5 pose categories.
  • boxes/trunc: tensor containing the numerical label that represents the index if the image is truncated.
  • boxes/difficult: tensor containing the numerical label that represents the index if the image is difficult to annotate.
  • parts/box: tensor that contains the coordinates of the bounding boxes for body parts used for the person layout challenge.
  • parts/label: tensor that contains the labels of the bounding boxes for body parts used for person layout challenge.
  • semantics/mask: tensor that represents the class mask.
  • semantics/label: tensor that contains the labels of classes present in the image and two extra labels, ‘background’ representing the background of the objects and ‘None’ representing the border/outline of the object or objects difficult to label.
  • metadata/main_test_set: test set used for object classification and detection.
  • metadata/part_test_set: test set used for person layout challenge.
  • metadata/segmentation_test_set: test set used for class and object segmentation.
  • metadata/image_meta: tensor containing image metadata.
Pascal VOC 2007 Data Splits
Altogether the dataset set has 9,963 images in all, with 24,640 labeled samples. The data has been divided into 50% for training/validation and 50% for testing.

How to use Pascal VOC 2007 Dataset with PyTorch and TensorFlow in Python

Train a model on Pascal VOC 2007 dataset with PyTorch in Python

Let’s use Deep Lake built-in PyTorch one-line dataloader to connect the data to the compute:

				
					dataloader = ds.pytorch(num_workers=0, batch_size=4, shuffle=False)
Train a model on Pascal VOC 2007 dataset with TensorFlow in Python 
				
					dataloader = ds.tensorflow()

Additional Information about Pascal VOC 2007 Dataset

Pascal VOC 2007 Dataset Description

Pascal VOC 2007 Dataset Curators
Mark Everingham, John Winn
Pascal VOC 2007 Dataset Licensing Information
Deep Lake users may have access to a variety of publicly available datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have a license to use the datasets. It is your responsibility to determine whether you have permission to use the datasets under their license.
 
If you’re a dataset owner and do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thank you for your contribution to the ML community!
Pascal VOC 2007 Dataset Citation Information 
				
					@article{everingham2009pascal,
title={The PASCAL visual object classes challenge 2007 (VOC2007) development kit},
author={Everingham, Mark and Winn, John},
year={2009},
publisher={Citeseer}
}
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