Microsoft Florence-2

Florence-2 is a state-of-the-art vision foundation model released by Microsoft Research, designed to handle a wide array of computer vision and vision-language tasks through a unified sequence-to-sequence architecture. Unlike traditional models that require task-specific heads, Florence-2 treats every vision task—from captioning and object detection to grounding and segmentation—as a translation problem. It utilizes a DaViT vision encoder to convert images into visual tokens, which are then processed alongside text prompts by a transformer-based multi-modal encoder-decoder. In the 2026 landscape, Florence-2 stands out as a premier choice for edge-AI and high-throughput enterprise pipelines due to its compact parameter count (232M for Base, 771M for Large) and its massive pre-training on the FLD-5B dataset. It offers near-zero latency for real-time applications where GPT-4o or Gemini-1.5-Pro would be cost-prohibitive. Its ability to generate precise spatial coordinates and detailed textual descriptions makes it a cornerstone for autonomous systems, automated document processing, and advanced digital asset management.