Use case
Image classification
Automatically categorize images to understand their content and patterns so apps and GenAI models can make accurate decisions based on image classifications.
Why Labelbox for image classification
Increase data quality
Generate high-quality, diverse datasets for image classification by combining advanced tooling, humans, AI, and on-demand services in one unified solution.
Accelerate time to value
Rapidly integrate data, create quality training data for image classification, and deploy models to production.
Access on-demand image classification expertise
Embrace on-demand, highly-skilled labeling services and industry-specific insights that help execute image classification tasks at scale.
Collaborate in real-time
Enjoy direct access to internal and external labelers with real-time feedback on image classification tasks via the Labelbox platform.
Understanding image classification
Image classification provides understanding in how different visual elements are represented, empowering apps and models to make accurate decisions based on an image.. These learned representations help generate new images from text that exhibit similar objects and features in their category.
Challenges in image classification tasks
The complexity of real-world data makes image classification a challenge, from variability in image categories such as size, lighting, environment to confusion between classes where objects appear visually similar.
Image classification with Labelbox
Labelbox empowers AI teams to train models that think, plan, and act intelligently. Our platform's flexibility and powerful annotation tools allow you to complete image classification tasks that enhance generative capabilities of AI models.
Customer spotlight
Labelbox's intuitive tooling coupled with post-training labeling services offered a collaborative environment where Speak's internal team, along with external data annotators, could work together seamlessly. Learn more about how Speak uses Labelbox to improving the quality and efficiency of their data labeling.
