Off-the-Shelf AI Training Datasets
Access hundreds of ready-to-use AI training datasets. The culmination of Appen’s 25+ years of expertise in multimodal data collection, transcription, and annotation.
What Are Off-the-Shelf
AI Datasets?
Pre-existing AI training datasets are a fast and affordable way to quickly deploy your model for a variety of use cases. The effectiveness of any AI model depends upon the quality and diversity of its training data and off-the-shelf datasets are a great way to access large amounts of data quickly and affordably.
290+
Datasets
80+
Languages
80+
Countries
10K+
Hours
80K+
Images
10M+
Words
Off-the-Shelf vs. Custom AI Training Datasets
The choice between off-the-shelf datasets and custom AI data collection depends on the specific requirements, budget, and timeline of your project. Off-the-shelf datasets are ideal for general applications where quick deployment and cost-effectiveness are priorities, while custom datasets are best suited for specialized tasks where precision, customization, and flexibility are essential for achieving superior performance.
Types of AI Training Datasets
AI data comes in many forms, with diverse options available to suit the needs of your project. Training your model on high-quality data is crucial to maximize your AI model’s performance.
Speech
Audio files with corresponding timestamped transcription for applications such as automatic speech recognition, language identification, and voice assistants.
Key features:
- Speech types: scripted (including TTS), conversational, broadcast
- Recording types: microphone, telephony (mobile, landline), smartphone
- Environments: quiet (home, office, studio), noisy (public place, in-car, roadside)
- Audio quality: 8kHz – 96kHz
Text
Tailored, ethically-sourced text datasets that drive smarter insights for more accurate language processing and machine learning models.
Text datasets include:
- Pronunciation Dictionaries (Lexicons): 5.4M words in 75 languages
- Part-of-speech (POS) dictionaries: 3.2M words in 18 languages
- Named Entity Recognition (NER): 344k+ entity labels in 9 languages
- Inverse Text Normalization: 36k+ test cases in 7 languages
Image
115k+ images in 14+ languages to develop diverse applications such as optical character recognition (OCR) and facial recognition software.
Featured image datasets include:
- 15.8K images of documents in 14 languages with mixed premium and challenging conditions for OCR
- 13.5K human facial images of 99 participants in various lighting conditions, angles, and expressions.
Video
High-quality video data to enhance AI models, like multi-modal LLMs, for tasks such as object detection, gesture recognition, and video summarization.
Featured video dataset:
- 130 sessions documenting human body movement of 100 diverse participants in the United Kingdom and the Philippines
- Multi-camera recordings in several locations with varied background, weather, and lighting conditions.
Location
Precise location data for insights into user movements and interactions with specific points of interest, enabling location-based analytics and targeted strategies.
- Accurate GPS signals collected in-app from SDKs
- Global: 200+ countries
- Compliant: 100% user opt in
- Scale: 1.5+ billion devices and 500+ billion events
Benefits of Using Pre-Existing
AI Training Datasets
Appen's datasets are carefully constructed through a detailed data annotation process and reviewed by experienced annotators to provide a reliable foundation for training models and performance across various applications.
Speed
Immediately available for rapid deployment
Cost
Licensed datasets are an economical solution
Quality
Developed by Appen’s internal data experts
How to Choose the Right
Data for Your AI Project
The most important factors to consider when selecting data for your AI project are the quality, size, and accuracy of the dataset. Make sure your data is ethically sourced to provide your model with reliable and diverse information.
How much data do you need to train AI?
The amount of data needed to train an AI model depends on the model type and task complexity. Simple models, like basic image recognition, may need thousands of labeled images, while complex tasks like NLP or advanced computer vision often require millions of data points. For example:
- Image Recognition: Training a basic model might require 10,000 to 100,000 images per class, whereas more complex models like those used in self-driving cars need millions of images.
- Natural Language Processing (NLP): Language models like GPT-3 were trained on hundreds of billions of words to achieve high accuracy across a wide range of tasks.
- Custom Models: Smaller datasets might work if the model is fine-tuned on pre-trained networks (transfer learning), but more data will generally lead to better performance.
Get Started with Off-the-Shelf AI Training Datasets
Appen’s extensive catalog of off-the-shelf (OTS) datasets spans multiple data types and industries, providing comprehensive coverage for various AI applications. These datasets are crafted to the highest standards of quality and accuracy, ensuring reliable training data for AI models.