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20 Best Pytorch Datasets to Build Efficient Deep Learning Models/ai-insights/20-best-pytorch-datasets-to-build-efficient-deep-learning-models

20 Best Pytorch Datasets to Build Efficient Deep Learning Models

July 11, 2023

20 Best Pytorch Datasets to Build Efficient Deep Learning Models

A qualified professional with a credible AI Certification or an AI consultant will be able to elucidate to you the role of possessing suitable datasets skills while building deep learning models. Let us discuss the popular PyTorch datasets that can help you create highly accurate predictions and analyze patterns in your data.

Understanding PyTorch:

PyTorch is a popular open-source machine learning framework; all thanks to its dynamic computation graphs, user-friendly interface, and top-notch support for GPUs. With these datasets, you will have everything you need to take your deep learning models to the next level.

So, whether you are interested in image recognition, natural language processing, healthcare, or any other AI domain, these PyTorch datasets are sure to offer valuable insights and opportunities for research.

Gear up to explore cutting-edge AI and see what you can achieve with the power of PyTorch!

  • MNIST: This is a classic dataset for image recognition tasks, containing images of handwritten digits from 0 to 9.
  • CIFAR-10: Another popular image recognition dataset, CIFAR-10, contains 10 different classes of objects, such as airplanes, cars, and animals.
  • ImageNet: One of the largest image recognition datasets, ImageNet contains millions of labeled images in over 22,000 categories.
  • COCO: This dataset is commonly used for object detection tasks, containing over 300,000 images with more than 2 million object instances labeled across 80 categories.
  • Cityscapes: A dataset for autonomous driving tasks, Cityscapes contains street scenes from various cities with pixel-level annotations for objects such as cars, pedestrians, and buildings.
  • Pascal VOC: Another popular object detection dataset, Pascal VOC, contains images from real-world scenes annotated with object bounding boxes and object class labels. This initiative offers consistent image datasets for recognizing object classes and a uniform set of tools for accessing the data and annotations. This facilitates the assessment and comparison of diverse approaches.
  • WikiText: A large-scale language modeling dataset containing over 100 million tokens from Wikipedia articles. If there is a comparison between Penn Treebank and WikiText-2, the latter is almost twice in size and number. Comparatively, WikiText -103 supersedes the rest by being 110 times larger.
  • Penn Treebank: A widely used dataset for natural language processing tasks, Penn Treebank contains parsed text from the Wall Street Journal.
  • SNLI: The Stanford Natural Language Inference dataset contains 570,000 sentence pairs labeled entailment, contradiction, or neutral. It supports the natural language inference system, which can other be called RTE (recognizing textual entailment).
  • SQuAD: The Stanford Question Answering Dataset contains questions posed on Wikipedia articles, with corresponding answer text spans.
  • 1MIMIC-III: A large-scale dataset of electronic health records, MIMIC-III contains data from over 40,000 patients with various clinical notes and diagnoses.
  • Fashion-MNIST: A variant of the MNIST dataset, Fashion-MNIST contains images of clothing items instead of handwritten digits. The Fashion-MNIST dataset comprises images of Zalando's clothing items, including 60,000 examples for training and 10,000 for testing.
  • CelebA: A dataset of celebrity faces with attributes such as age, gender, and facial expressions. This dataset helps various applications authenticate face recognition as their security systems. MMLAB of Hong Kong invented the original data for this dataset.
  • Kinetics: A dataset of human action recognition, Kinetics contains more than 50,000 video clips of people performing various actions such as walking, running, and dancing. Each video clip has a time duration of 10 secs and highlights 600 sets of human actions.
  • Open Images: A large-scale dataset for object detection tasks, Open Images contains millions of images with annotations for over 600 classes of objects.
  • LJSpeech: A dataset for text-to-speech synthesis, LJSpeech contains 13,1000 short audio recordings of a single speaker reading sentences from a newspaper. The speaker has picked up sections from 7 non-fictional books.
  • LibriSpeech: A dataset for speech recognition tasks, LibriSpeech contains audio recordings that can sum beyond 1000 hours and are a part of LibriVox audiobooks with corresponding transcripts.
  • AudioSet: A dataset for audio event recognition, AudioSet contains audio recordings labeled with over 527 classes of sounds. These sound clips are of 10-sec duration. It has been organized by using the help of youtube metadata and research-based content.
  • NSynth: A dataset for musical instrument synthesis, NSynth contains audio recordings of various musical instruments with corresponding pitch and timbre information. It is a collective group of tunes assembled from 1006 instruments producing 305979 melodious tunes.
  • Chess: A dataset for chess game prediction containing data from thousands of games with information such as player ratings and move sequences. It has information about more than 20,000 game collections and how to add more.

Conclusion

Go ahead and dive into these datasets, and start building your own cutting-edge AI applications today. And if you are looking to become a certified AI professional, do not forget to explore the top AI certification courses available. By leveraging these resources and continuing to push the boundaries of AI research, we can unlock new frontiers in technology and make the world a better place.

With these datasets and PyTorch's powerful tools and libraries, you can train efficient and accurate deep-learning models tailored to your needs. This makes PyTorch an invaluable resource for AI consultants, data scientists, and anyone looking to break into the field of machine learning.