What is a Deep Learning Video Dataset?

Deep learning requires a lot of practice to improve (or other professions in life). Video processing and speech recognition are only a few of the topics covered. Each of these problems has its own personality and approach.

Where do you get this information, though? Many of the research articles you read these days rely on proprietary datasets that aren’t widely available. This becomes a problem if you wish to learn and put your newly acquired skills to work.

If you’re having trouble with this, we’ve got a solution for you. We’ve prepared a list of publicly available datasets for your convenience.

We’ve put up a list of high-quality datasets that any deep learning enthusiast may use to practise and develop their skills in this post.

Working with these datasets will help you develop your data scientist abilities, and the information you learn will be useful in your career. We’ve also included articles with state-of-the-art (SOTA) results for you to go through and utilise to improve your models.

What can you do with these Deep LearningVideo Datasets? 

To begin with, these datasets are enormous! As a result, for deep learning, make sure you have a fast internet connection with no or a very big data download limit.

These datasets can be utilised in a number of different ways. They may be used to accomplish a number of different Deep Learning techniques. You may utilize them to develop your abilities, learn how to identify and organize each challenge, come up with original use cases, and share your results with the world!

The three categories of datasets are video processing, natural language processing, and audio/speech processing.

 is a list of the top 8Video Dataset ForDeep Learning:

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  1. ImageNet (http://www.image-net.org/)


ImageNet is a collection of pictures arranged according to the WordNet hierarchical structure. WordNet includes roughly 100,000 phrases, while ImageNet has contributed around 1000 photos to accompany each word on average.


Dimensions: 150 GB


The total number of records is: There are around 1,500,000 pictures in total, each with numerous bounding boxes and class labels.


SOTA: Deep Neural Network Aggregated Residual Transformations (https://arxiv.org/pdf/1611.05431.pdf)


  1. MS-COCO (http://cocodataset.org/#home)


COCO is a large-scale dataset with a lot of potential for object recognition, segmentation, and captioning. It has the following characteristics:


  • Segmentation of objects
  • In-context recognition
  • Segmentation of superpixel items
  • 330K pictures (>200K of which are labeled)
  • 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
  • Approximately 25 GB in size (Compressed)


250,000 individuals with important points, 330K photos, 80 object categories, 5 descriptions each image, 330K images, 80 object categories, 5 captions per image.


SOTA: R-CNN Mask (https://arxiv.org/pdf/1703.06870.pdf)


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  1. MNIST (https://datahack.analyticsvidhya.com/contest/practice-problem-identify-the-digits/)


MNIST is one of the most widely used deep learning datasets. It’s a dataset of handwritten digits with 60,000 examples in the training set and 10,000 examples in the test set.


It’s a fantastic database for experimenting with deep learning algorithms and patterns on real-world data while spending as little time and effort as possible on data preparation.


Approximately 50 MB in size


There are 70,000 pictures in ten classifications, therefore there are a lot of records.


SOTA: Capsule-to-Capsule Dynamic Routing (https://arxiv.org/pdf/1710.09829.pdf)


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  1. Fashion-MNIST (https://github.com/zalandoresearch/fashion-mnist)


Fashion-MNIST is made up of 60,000 training photos and 10,000 test images. It’s a fashion product database similar to MNIST. Because the creators feel MNIST is overused, they built this as a straight alternative for it. Each video is in greyscale and has a label from one of ten classifications.

Fashion-MNIST: Revolutionizing Fashion Image Recognition

Introduction: Fashion-MNIST is a groundbreaking dataset that has revolutionized the field of fashion image recognition. It serves as a benchmark for evaluating the performance of machine learning algorithms and models in classifying fashion-related images. Developed as an alternative to the traditional MNIST dataset, which focuses on handwritten digits, Fashion-MNIST provides a diverse range of clothing items for training and testing image recognition algorithms. In this article, we will explore the significance of Fashion-MNIST and its impact on the fashion industry and machine learning research.

  1. The Need for Fashion-Specific Image Recognition: Fashion is a dynamic and ever-evolving industry that relies heavily on visual aesthetics. The ability to automatically classify and recognize fashion items in images is crucial for various applications, such as e-commerce, fashion recommendation systems, and inventory management. Traditional image recognition datasets, like the MNIST dataset, were limited in their scope and did not adequately capture the complexities of fashion-related images. Fashion-MNIST was created to address this gap and provide a more relevant dataset for training and evaluating fashion image recognition algorithms.
  2. The Structure and Diversity of Fashion-MNIST: Fashion-MNIST consists of a collection of 60,000 training images and 10,000 testing images, each measuring 28×28 pixels. The dataset encompasses ten different categories of clothing items, including T-shirts, dresses, sandals, sneakers, and more. This diversity enables researchers to develop models that can accurately classify a wide range of fashion items. The dataset is balanced, meaning that each category has an equal number of samples, ensuring that algorithms are tested on an unbiased distribution of fashion images.
  3. Evaluating Machine Learning Algorithms: Fashion-MNIST serves as a benchmark for evaluating the performance of machine learning algorithms in fashion image recognition tasks. Researchers can use the dataset to compare and analyze the accuracy, efficiency, and generalization capabilities of different algorithms. The goal is to develop models that can achieve high accuracy in classifying fashion items, enabling applications like automatic product tagging, visual search, and style recommendation systems.
  4. Advancements in Deep Learning Techniques: Fashion-MNIST has spurred advancements in deep learning techniques for fashion image recognition. Deep learning models, such as convolutional neural networks (CNNs), have achieved remarkable performance on the dataset. Researchers have developed sophisticated CNN architectures and optimization strategies to improve accuracy and efficiency in classifying fashion items. These advancements have not only enhanced fashion image recognition but have also influenced other areas of computer vision and pattern recognition.
  5. Real-World Applications in the Fashion Industry: The impact of Fashion-MNIST extends beyond the realm of research and academia. The dataset has paved the way for practical applications in the fashion industry. E-commerce platforms use fashion image recognition algorithms to automatically tag and categorize products, enabling faster and more accurate search results. Fashion recommendation systems leverage the capabilities of image recognition to suggest relevant items based on users’ preferences and style. Additionally, inventory management systems utilize image recognition to track and organize fashion items efficiently.
  6. Transfer Learning and Generalization: Fashion-MNIST has also facilitated advancements in transfer learning and generalization. Transfer learning techniques allow models trained on Fashion-MNIST to be fine-tuned and applied to other fashion-related datasets or even different image recognition tasks. This ability to generalize and transfer knowledge across domains reduces the need for extensive training on new datasets, saving time and computational resources.
  7. Inspiring Further Research and Dataset Development: Fashion-MNIST has inspired further research and the development of new fashion-specific datasets. Researchers have expanded on the concept of Fashion-MNIST, creating larger and more diverse datasets that include additional fashion attributes like color, texture, and style. These datasets enable the development of more comprehensive and accurate fashion image recognition models.

Conclusion: Fashion-MNIST has revolutionized the field of fashion image recognition, providing a benchmark dataset for evaluating machine learning algorithms and models. Its structure and diversity have facilitated advancements in deep learning techniques, transfer learning, and generalization. The real-world applications of fashion image recognition in e-commerce, recommendation systems, and inventory management highlight the practical significance of the dataset. Furthermore, Fashion-MNIST has inspired further research and the development of new fashion-specific datasets. As the fashion industry continues to evolve, Fashion-MNIST will remain a valuable resource for driving innovation and advancements in fashion image recognition.

30 MB in size


There are 70,000 pictures in ten classifications, therefore there are a lot of records of deep learning.


SOTA: Random Erasing Data Augmentation (https://arxiv.org/abs/1708.04896)


  1. CIFAR-10 (http://www.cs.toronto.edu/~kriz/cifar.html)


This is another picture categorization dataset. It has 60,000 pictures divided into ten categories (each class is represented as a row in the above image). There are 50,000 training and 10,000 test pictures in all. The data is split into six sections: five training batches and one test batch. There are 10,000 pictures in each batch in deep learning.


170 megabytes


There are 60,000 pictures in ten classifications, therefore there are a lot of records.


SOTA: ShakeDrop regularisation (https://openreview.net/pdf?id=S1NHaMW0b)


  1. SVHN (http://ufldl.stanford.edu/housenumbers/)


This is a collection of real-world images that may be used to test object detection systems. This necessitates the least amount of data preparation. It’s comparable to the MNIST dataset described before, except it includes more tagged data (over 600,000 images). Google Street View house numbers were used to compile the data of deep learning.


2.5 GB in size


There are 6,30,420 pictures in ten classifications, for a total of 6,30,420 records.


SOTA: Virtual Adversarial Training and Distributional Smoothing (https://arxiv.org/pdf/1507.00677.pdf)


  1. VisualQA (http://www.visualqa.org/)


VQA is a collection of open-ended image-related questions. These questions need knowledge of vision and language. The following are some of the dataset’s most intriguing features of deep learning:


  • There are 265,016 pictures in all (COCO and abstract scenes)
  • Per image, at least 3 questions (on average, 5.4 questions) are asked.
  • For each question, there are ten ground-truth answers.
  • Per question, there are three viable (but most likely wrong) responses.
  • Evaluation by a computer metric


Dimensions: 25 GB (Compressed)


Number of Records: 265,016 pictures, each with at least three questions and ten ground-truth answers.


SOTA: Visual Question Answering Tips & Tricks: Learnings from the 2017 Challenge (https://arxiv.org/abs/1708.02711)


  1. Open Images datasets (https://github.com/openimages/dataset)


Open Video is a collection of almost 9 million picture URLs. Image-level labels enclosing boxes covering hundreds of classes have been tagged on these pictures for deep learning. There are 9,011,219 photographs in the training set, 41,260 images in the validation set, and 125,436 images in the test set.


Dimensions: 500 GB (Compressed)


There are 9,011,219 pictures with more than 5,000 labels in the database.


SOTA: Model checkpoint, Checkpoint readme, Inference code, Resent 101 video classification model (trained on V2 data). (https://github.com/openimages/dataset/blob/master/tools/classify_oidv2.py)



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