PUCPR Dataset

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Visualization of the PUCPR dataset in the Activeloop Platform

PUCPR dataset

What is PUCPR Dataset?

The Pontifical Catholic University of Parana+ Dataset (PUCPR) is a large-scale dataset with around 17,000 images focused on car counting in the scenes of diverse parking lots. The images in the dataset are collected with the drone view at approximately 40 meters in height. The image set is annotated by a bounding box per car. All labeled bounding boxes have been well recorded with the top-left points and the bottom-right points.

Download PUCPR Dataset in Python

Instead of downloading the PUCPR dataset in Python, you can effortlessly load it in Python via our open-source package Hub with just one line of code.

Load PUCPR Dataset Training Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/pucpr-train")

Load PUCPR Dataset Testset Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/pucpr-test")

PUCPR Dataset Structure

PUCPR Data Fields

image: tensor containing the image.
boxes: tensor to identify cars using a bounding box.
labels: tensor to label identifies the object as a car.
scenes: tensor to identify the climate in which the image is taken.

PUCPR Data Splits

The PUCPR dataset training set is composed of 95.
The PUCPR dataset testing set is composed of 25.

How to use PUCPR Dataset with PyTorch and TensorFlow in Python

Train a model on the PUCPR 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 the PUCPR dataset with TensorFlow in Python

				
					dataloader = ds.tensorflow()

Additional Information about PUCPR Dataset

PUCPR Dataset Description

Homepage: https://lafi.github.io/LPN/
Repository: N/A
Paper: Meng-Ru Hsieh, Yen-Liang Lin, Winston H. Hsu. Drone-based Object Counting by Spatially Regularized Regional Proposal Networks, ICCV 2017
Point of Contact: N/A

PUCPR Dataset Curators

Meng-Ru Hsieh, Yen-Liang Lin, Winston H. Hsu

PUCPR 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!

PUCPR Dataset Citation Information

				
					@inproceedings{Hsieh_2017_ICCV,
Author = {@inproceedings{Hsieh_2017_ICCV,
Author = {Meng-Ru Hsieh and Yen-Liang Lin and Winston H. Hsu},
Booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
Title = {Drone-based Object Counting by Spatially Regularized Regional Proposal Networks},
Year = {2017},
organization={IEEE}
}},
Booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
Title = {Drone-based Object Counting by Spatially Regularized Regional Proposal Networks},
Year = {2017},
organization={IEEE}
}

PUCPR Dataset FAQs

What is the PUCPR dataset for Python?

The Pontifical Catholic University of Parana+ Dataset (PUCPR) dataset contains images from a drone view at approximately 40 meters in height. It has around 17,000 images focused on car counting in the scenes of diverse parking lots. The image dataset is annotated by a bounding box per car.

How can I use the PUCPR dataset in PyTorch or TensorFlow?

You can stream the PUCPR dataset while training a model in PyTorch or TensorFlow with one line of code using the open-source package Activeloop Deep Lake in Python. See detailed instructions on how to train a model on the PUCPR dataset with PyTorch in Python or train a model on the PUCPR dataset with TensorFlow in Python.