Tencent AI Lab: YOLO World

Grounding DINO is an open-vocabulary object detection model developed by IDEA Research, released in March 2023 under the Apache 2.0 license. It extends the DINO transformer-based detector with grounded pre-training, enabling it to detect arbitrary objects described by free-form text queries rather than a fixed set of predefined categories. The model integrates a text encoder with a visual backbone through a feature fusion module that aligns language and visual representations at multiple scales.Grounding DINO achieves strong zero-shot detection performance on COCO, LVIS, and ODinW benchmarks, and supports referring expression comprehension tasks. It is widely used as a foundation for open-vocabulary detection pipelines and as the detection backbone in systems such as Grounded-SAM. The model is particularly suited for applications requiring flexible, text-driven object localization across diverse domains.

Florence-2, introduced by Microsoft Research at CVPR 2024, is an open-source vision-language foundation model designed to unify diverse computer vision tasks within a single sequence-to-sequence framework. Unlike traditional models that specialize in specific tasks, Florence-2 accepts both images and text prompts and outputs text for tasks such as captioning, object detection, segmentation, OCR, and region-based grounding. It comes in two sizes—Florence-2-base (~230M parameters) and Florence-2-large (~770M parameters)—and is trained on FLD-5B, a large dataset of ~126M images with ~5.4B annotations.The model demonstrates strong zero-shot and fine-tuned performance, often rivaling larger vision-language systems while remaining lightweight and efficient. Released under the MIT license, all weights are publicly available, making it accessible for fine-tuning and deployment in applications like VQA, content tagging, accessibility, and research. Florence-2’s compact design, versatility, and openness position it as a practical alternative to larger proprietary multimodal models.

OWL-ViT (Open-World Localization with Vision Transformers) is an open-vocabulary object detection model released in May 2022 by Google Research. It adapts a pretrained CLIP-style image-text model by removing the final pooling layer and attaching lightweight classification and box prediction heads to each Transformer output token, producing a detector capable of localizing arbitrary objects described by free-form text at inference time. Rather than being restricted to a fixed taxonomy such as the 80 categories in Microsoft COCO, OWL-ViT can detect object classes specified by a user's text query, including categories the model was never explicitly trained on.OWL-ViT accepts an image and a list of text queries as input, and produces bounding boxes with class assignments drawn from the supplied queries. It also supports one-shot image-conditioned detection, where a cropped image region is used as the query instead of text, allowing the model to find visually similar instances within a target scene. The model is released in multiple Vision Transformer sizes (ViT-B/32, ViT-B/16, ViT-L/14) and CLIP-pretrained variants, distributed through the Google Research scenic repository and Hugging Face under the Apache 2.0 license. A successor model, OWLv2, was released in June 2023, introducing the OWL-ST self-training recipe that scales training to over one billion pseudo-annotated examples and substantially improves detection performance on rare and long-tail categories while preserving the open-vocabulary interface.

YOLOE (YOLO with Everything) is an open-vocabulary object detection and segmentation model developed by THU-MIG at Tsinghua University, released in March 2025 under the AGPL-3.0 license. It extends the YOLO architecture to support open-vocabulary detection through text and visual prompts, enabling the model to detect arbitrary object categories beyond a fixed training set without retraining. The design integrates prompt encoding directly into the YOLO framework while preserving real-time inference speed.YOLOE is evaluated on COCO and LVIS benchmarks and supports both closed-set and open-vocabulary detection modes. It is built on the Ultralytics codebase and maintains compatibility with standard YOLO training and export workflows. YOLOE is suited for applications requiring flexible, prompt-driven object detection where the target object vocabulary may change at inference time.

Grounded SAM is an open-vocabulary image segmentation model developed by IDEA Research, released in January 2024 under the Apache 2.0 license. It combines Grounding DINO, a zero-shot open-vocabulary object detector, with the Segment Anything Model to produce precise segmentation masks for objects identified through free-form text prompts. The two models are used sequentially: Grounding DINO localizes objects from a text query, and SAM generates the corresponding segmentation masks.Grounded SAM enables zero-shot instance segmentation without task-specific training data, making it applicable to domains where labeled segmentation data is scarce. It supports arbitrary text queries and can segment objects not represented in standard training sets. The model is commonly used in automated labeling pipelines, robotic perception, and domain-specific vision applications requiring open-vocabulary segmentation.