Top Open Machine Learning Datasets That You Should Know
Part three of the Machine Learning datasets series continues where the past two parts left off, focusing on where to get the correct picture dataset to train your Machine Learning models.
Part three of the Learning dataset series focuses on finding the correct Image Database to train your Learning models, following up on the previous two sections.
This website contains a variety of datasets and links to portals where you may locate the perfect picture database for your project. Enjoy!
List of top 9 Open Machine Learning Datasets:
A big annotated Image Database may be found on this website.
However, downloading them is not simple. The dataset may be downloaded in two ways:
1. Using the LabelMe Matlab toolbox to download all of the pictures. You can customize the section of the database you wish to download using the toolbox.
2. Using the LabelMe Matlab toolbox to use the pictures from the internet. This is a less favored method since it is slower, but it allows you to see the Image Database for Machine Learning datasets before downloading it. After you’ve installed the database, you may read the annotation files and query the pictures with the LabelMe Matlab toolbox to extract specific items.
2. ImageNet (http://image-net.org/)
The picture collection for new algorithms is organized according to the WordNet hierarchy, with hundreds of thousands of photos depicting each node of the network.
To download Image Machine Learning datasets, you must first register on the site, then mouse over the ‘download’ menu dropdown and choose ‘original pictures.’ You can request access to the original pictures if you’re utilizing the datasets for educational or personal purposes.
ImageNet is also hosting a competition on Kaggle right now – check it out here.
Get Database: http://image-net.org/
3. LSUN (http://lsun.cs.princeton.edu/2016/)
This dataset is useful for scene comprehension in conjunction with auxiliary task initiatives (room layout estimation, saliency prediction, etc.).
The massive Image Machine Learning datasets, which includes photos from several rooms (as shown above), may be downloaded by going to the website and running the script supplied, which can be found here.
Scroll down below the scene classification’ heading and click ‘README’ to view the documentation
and demo code for additional information about the dataset.
Get Database: https://github.com/fyu/lsun/blob/master/download.py
4. MS COCO (http://mscoco.org/)
COCO is a large-scale dataset for detecting, segmenting, and labeling objects in context.
The dataset, as its name implies, comprises a wide range of everyday items that we see in our daily lives, making it suitable for training Machine Learning datasets models.
The following aspects of the Image Database are described on the website:
• Segmentation of objects
• In-context recognition
• Segmentation of superpixel items
• 330K pictures (>200K of which are labelled)
• 1.5 million instances of objects
• There are 80 different kinds of items to choose from.
• There are 91 different types of things.
• There are five captions per picture.
• 250,000 persons with important information
You will not be required to register or provide any personal information in order to access the dataset. You may either visit this page or use the links below to download them directly.
Get Database: http://images.cocodataset.org/zips/train2014.zip
The Columbia University Image Library collection contains 100 distinct things that have been photographed from every angle in a 360° rotation, ranging from toys to personal care items to tablets.
To get the dataset, you don’t need to register or provide any information on the website, making it a simple procedure. Simply click the link below to get the Machine Learning datasets in its entirety.
more like this, just click on: https://24x7offshoring.com/blog/
6. Visual Genome (http://visualgenome.org/)
This dataset gateway is a comprehensive visual knowledge base with captions for 108,077 Machine Learning datasets ranging from people to buildings to signs and everything in between.
The following features are described on the website:
• 108,077 Photographs
• 5.4 MILLION DESCRIPTIONS OF REGIONS
• 1.7 MILLION ANSWERS TO VISUAL QUESTIONS
• 3.8 Million Instances of Objects
• There are 2.8 million attributes in the database.
• There are 2.3 million relationships in the world.
To get the datasets provided, you do not need to leave any information or register; simply click the link below to visit the website and download the objects, relationships, and aliases you require.
Get Database: http://visualgenome.org/api/v0/api_home.html
7. Google’s Open Images (https://storage.googleapis.com/openimages/web/download.html)
A total of 9 million pictures have been tagged with image-level labels and object bounding boxes in this dataset.
V4’s training set includes 14.6 million bounding boxes for 600 item types on 1.74 million pictures, making it the world’s biggest dataset containing object position annotations in Machine Learning datasets.
Fortunately, you won’t need to register or provide any personal information to access the dataset, allowing you to download it immediately from the website.
8. Labelled Faces In The Wild (http://vis-www.cs.umass.edu/lfw/)
This portal offers 13,000 annotated pictures of human faces that you may use in your facial recognition Machine Learning datasets applications.
Simply click on the link below to access the dataset. You’ll see a sub-header labeled ‘Download the Database,’ where you may choose which file to download for use in your projects.
You won’t have to register or leave your information to access the Image Database, making it super simple to acquire the files you need and start working on your projects!
Get Database: http://vis-www.cs.umass.edu/lfw/#download
9. Stanford Dogs Database (http://vision.stanford.edu/aditya86/ImageNetDogs/)
There are 20,580 pictures and 120 distinct dog breed categories in this collection.
This dataset from Stanford was created using pictures from ImageNet and comprises photographs of 120 different dog breeds from across the world. For the goal of fine-grained picture classification, this dataset was created utilizing images and annotation from ImageNet for Machine Learning datasets.
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