Label Studio

Label Studio offers a wide range of annotation capabilities across different data modalities. For computer vision, it supports image classification, object detection, object tracking, and semantic segmentation. For NLP and documents, it provides PDF and image OCR, named entity recognition, question answering, and sentiment analysis. Audio and speech features include transcription, waveform/spectrogram support, speaker diarization, and emotion recognition. For time series, it offers classification, segmentation, and event recognition. Additionally, it supports multi-modal tasks like dialogue processing and optical character recognition combined with text. The platform provides programmable interfaces, including API, Python SDK, and webhooks, for integration into ML pipelines.

Label Studio is used for a variety of AI evaluation and data labeling tasks. It supports LLM and agent evaluation, including agentic traces, RLHF and fine-tuning, LLM evaluations with custom benchmarks, and RAG retrieval QA. It is also used for computer vision tasks, document AI, NLP, audio transcription, and time series analysis. The platform enables human-in-the-loop review by connecting observability tools and allows for AI-assisted labeling by connecting models. It is suitable for teams building AI models that require high-quality labeled data.

The page does not provide specific pricing information. Label Studio is available as an open source download, and there is a Label Studio Enterprise version mentioned in a blog post, but no pricing details are given.