How to download a machine learning dataset for PyTorch and TensorFlow in Python

Without any need to download, a variety of popular machine learning datasets can be accessed and streamed with Hub with one line of code. This enables you to explore the datasets and train models without needing to download machine learning datasets regardless of their size. Access classical datasets like CIFAR-10, MNIST or Fashion-MNIST, as well as large datasets like Google Objectron, ImageNet, COCO, and many others in Python.

What is MNIST Dataset?

The MNIST (Modified National Institute of Standards and Technology database) dataset contains a training set of 60,000 images and a test set of 10,000 images of handwritten digits. The handwritten digit images have been size-normalized and centered in a fixed size of 28×28 pixels. The MNIST digits dataset is often used by data scientists who want to try machine learning techniques and pattern recognition methods on real-world data while spending minimal effort on preprocessing and formatting.

What is COCO Dataset?

The COCO (Common Objects in Context) dataset comprises 91 common object categories, 82 of which have more than 5,000 labeled examples. The dataset has 2.5 million labeled instances in 328k photos, created with the help of a large number of crowd workers using unique user interfaces for category detection, instance spotting, and instance segmentation. COCO dataset is a novel dataset with the purpose of improving the state-of-the-art in object detection by aligning the problem within the larger framework of scene understanding.

What is CIFAR 10 Dataset?

The CIFAR10 (Canadian Institute For Advanced Research) dataset consists of 10 classes with 6000 color images of 32×32 resolution for each class. It is divided into 50000 training and 10000 testing images. The test dataset contains exactly 1000 randomly collected images from each class. The training datasets may contain more images per class compared to others but they contain 5000 images per class on average.

What is CIFAR 100 Dataset?

The CIFAR100 (Canadian Institute For Advanced Research) dataset consists of 100 classes with 600 color images of 32×32 resolution for each class. It is divided into 500 training and 100 testing images per class. In CIFAR100, there are 20 superclasses sub-grouped into 100 classes. The dataset comes with two labels for each image such as a “fine” label (class) and a “coarse” label (superclass).

What is the MNIST dataset used for

MNIST is used as a “hello world” example by data scientists worldwide. Typically, MNIST dataset is used as a benchmark dataset, or as a proof-of-concept for training and testing purposes in the field of machine learning.

What is the MNIST dataset for Python?

The MNIST dataset (Modified National Institute of Standards and Technology database) is one of the most popular datasets in machine learning. MNIST is a dataset of 60,000 square 28×28 pixel images of handwritten single digits between 0 and 9. The images are in grayscale format.

How to download the MNIST dataset in Python?

You can load MNIST dataset fast with one line of code using the open-source package Activeloop Hub in Python. See detailed instructions on how to load MNIST dataset training subset or MNIST dataset testing subset in Python.

How to create an Image Dataset like MNIST dataset?

With Activeloop Hub, creating image datasets like the MNIST digits dataset is simple. Simple datasets like MNIST can be created automatically by allowing Hub parse the legacy files into Hub dataset format. More complex datasets can be created manually.

What is the size of each image in the MNIST dataset?

MNIST dataset image size is constant across all images of the dataset. Each MNIST dataset image is a fixed-size 28×28 pixel square image.

MNIST vs Fashion-MNIST. What is the difference between MNIST and Fashion-MNIST?

MNIST and Fashion-MNIST dataset are two separate datasets. However, the Fashion-MNIST dataset is meant to be an MNIST dataset alternative. Fashion-MNIST comprises pictures of clothing items and was published in 2017 by Zalando, a German online retailer. Both datasets are of the same size: 60 000 photos in the training set, as well as 10 000 pictures of clothing in the validation set of the dataset.

What does MNIST stand for?

MNIST is an acronym for the Modified National Institute of Standards and Technology dataset.

Why is MNIST so popular?

Created in 1998, MNIST dataset containing handwritten digits from 0-9 is small in size, simple in structure and easy to use. This makes it a good first dataset to try various pattern recognition methods on, with minimal effort required on data preprocessing..

Is MNIST suitable for deep learning?

Yes, MNIST data set is one of the most used datasets for deep learning. As of July 2022, MNIST only falls behind CIFAR-10, Imagenet, coco datasets in the amount of papers that cite them (or has used as a benchmark dataset.

Google Objectron Dataset

Estimated reading: 8 minutes

Visualization of the Objectron bike training set dataset in the Deep Lake UI

Objectron dataset

What is Objectron Dataset?

The Objectron dataset consists of several short, object-centric video clips where the camera steadily moves around the object and captures it from different angles. The videos contain manually annotated 3D bounding boxes describing the object’s position, orientation, and dimensions. The dataset also comes with the metadata from AR sessions, including camera poses, sparse point clouds, and characterization of the planar surfaces from the surrounding environment. The dataset is collected from 10 countries across five continents, thus ensuring geo-diversity. It consists of 17,095 object instances with 14,819 annotated video clips complemented with 4M annotated images in the following categories: bikes, books, cameras, cereal boxes, chairs, cups, laptops, and shoes.

Download Objectron Dataset in Python

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

Load Objectron Dataset Training Subset for the Category Book in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/objectron_book_train")

Load Objectron Dataset Testing Subset for the Category Book in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/objectron_book_test")

Load Objectron Dataset Training Subset for the Category Bike in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/objectron_bike_train")

Load Objectron Dataset Testing Subset for the Category Bike in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/objectron_bike_test")

Load Objectron Dataset Training Subset for the Category Bottle in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/objectron_bottle_train")

Load Objectron Dataset Testing Subset for the Category Bottle in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/objectron_bottle_test")

Load Objectron Dataset Training Subset for the Category Camera in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/objectron_camera_train")

Load Objectron Dataset Testing Subset for the Category Camera in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/objectron_camera_test")

Load Objectron Dataset Training Subset for the Category Cereal box in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/objectron_cereal_box_train")

Load Objectron Dataset Testing Subset for the Category Cereal box in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/objectron_cereal_box_test")

Load Objectron Dataset Training Subset for the Category Chair in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/objectron_chair_train")

Load Objectron Dataset Testing Subset for the Category Chair in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/objectron_chair_test")

Load Objectron Dataset Training Subset for the Category Cup in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/objectron_cup_train")

Load Objectron Dataset Testing Subset for the Category Cup in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/objectron_cup_test")

Load Objectron Dataset Training Subset for the Category Shoe in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/objectron_shoe_train")

Load Objectron Dataset Testing Subset for the Category Shoe in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/objectron_shoe_test")

Load Objectron Dataset Training Subset for the Category Laptop in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/objectron_laptop_train")

Load Objectron Dataset Testing Subset for the Category Laptop in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/objectron_laptop_test")

Objectron Dataset Structure

Objectron Data Fields

camera_extrinsics: tensor containing row major 4×4 transformation matrix describing the camera pose w.r.t. the world origin. The world origin is where the AR session has started.
camera_intrinsics: tensor containing row-major 3×3 intrinsic matrix describing the focal length and the principal point of the camera.
camera_projection: tensor containing row major 4×4 projection matrix.
camera_view: tensor containing row major 4×4 view matrix.
object_orientation: tensor containing string indicating the orientation of the camera (portrait, landscape).
object_scale: tensor containing list of object scales.
object_translation: tensor containing list of object translations.
object_visibility: tensor containing list of annotation visibilities.
plane_center: tensor containing center point for the ground plane where objects are sitting.
plane_normal: tensor containing the normal vector to the ground plane where objects are sitting.
point_2d: tensor containing float numbers of all 2D points.
point_3d: tensor containing float numbers of all 3D points.
point_num: tensor containing point numbers for each instance. The bounding box has 9 points, skeleton may have a varied number of points.
image_channels: an integer representing number of channels in the image.
image_height: an integer representing height of the image.
image_id: an integer representing frame number in the sequence.
image_timestamp: an integer representing the microsecond timestamp of the video frame in the video stream.
image_width: an integer representing height of the image.
instance_num: an integer representing number of object instances in this frame.
image: tensor containing an image of dimension (image_height x image_width x image_channels).

Objectron Data Splits

Google Objectron dataset training set for class book is composed of 460838 images.
Google Objectron dataset testing set for class book is composed of 115389 images.
Google Objectron dataset training set for class bike is composed of 123675 images.
Google Objectron dataset testing set for class bike is composed of 30714 images.
Google Objectron dataset training set for class bottle is composed of 378932 images.
Google Objectron dataset testing set for class bottle is composed of 93888 images.
Google Objectron dataset training set for class camera is composed of 179175 images.
Google Objectron dataset testing set for class camera is composed of 45939 images.
Google Objectron dataset training set for class cereal_box is composed of 314910 images.
Google Objectron dataset testing set for class cereal_box is composed of 76827 images.
Google Objectron dataset training set for class chair is composed of 394529 images.
Google Objectron dataset testing set for class chair is composed of 97621 images.
Google Objectron dataset training set for class cup is composed of 436513 images.
Google Objectron dataset testing set for class cup is composed of 109870 images.
Google Objectron dataset training set for class shoe is composed of 445919 images.
Google Objectron dataset testing set for class shoe is composed of 115019 images.
Google Objectron dataset training set for class laptop is composed of 388792 images.
Google Objectron dataset testing set for class laptop is composed of 97074 images.

Google Objectron dataset testing set for class laptop is composed of 97074 images.

How to use Objectron Dataset with PyTorch and TensorFlow in Python

Train a model on Objectron 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 Objectron dataset with TensorFlow in Python

				
					dataloader = ds.tensorflow()

Additional Information about Objectron Dataset

Objectron Dataset Description

Homepage: https://www.objectron.dev/
Repository: https://github.com/google-research-datasets/Objectron
Paper: https://arxiv.org/abs/2012.09988
Point of Contact: objectron@google.com, objectron@googlegroups.com

Objectron Dataset Curators

Adel Ahmadyan, Liangkai Zhang, Jianing Wei, Artsiom Ablavatski, Matthias Grundmann

Objectron Dataset Licensing Information

Computational Use of Data Agreement 1.0 (C-UDA-1.0) license

Objectron Dataset Citation Information

				
					@article{objectron2021,
  title={Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations},
  author={Adel Ahmadyan, Liangkai Zhang, Artsiom Ablavatski, Jianing Wei, Matthias Grundmann},
  journal={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2021}
}

Objectron Dataset FAQs

What is the Objectron Dataset for Python?

The Objectron Dataset was introduced to advance state of the art in 3D object detection. It consists of object-centric 15K annotated videos and 4M annotated images.

What is the Objectron Dataset used for?

The Objectron Dataset is commonly used to improve 3D shape representation and enable new research and applications in 3D understanding, video models, object retrieval, view synthetics, and 3D reconstruction.

What are the Google Objectron Dataset classes?

The Objectron Dataset consists of 9 different classes: bikes, books, cameras, cereal boxes, chairs, cups, laptops, and shoes.

How to download the Objectron Dataset in Python?

You can load Objectron Dataset fast with one line of code using the open-source package Activeloop Deep Lake in Python. See detailed instructions on how to load Objectron Dataset training subset or Objectron Dataset testing subset in Python.

How can I use Objectron Dataset in PyTorch or TensorFlow?

You can stream Objectron 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 Objectron Dataset with Pytorch in Python or train a model on Objectron Dataset with TensorFlow in Python.