1. Model Details 2. Model Sources 3. How to Get Started with the Model 4. Training Details 5. Evaluation 6. Model Architecture and Objective 7. Citation > We present in this paper a novel query formulation using dynamic anchor boxes for DETR (DEtection TRansformer) and offer a deeper understanding of the role of queries in DETR. This new formulation directly uses box coordinates as queries in Transformer decoders and dynamically updates them layer-by-layer. Using box coordinates not only helps using explicit positional priors to improve the query-to-feature similarity and eliminate the slow training convergence issue in DETR, but also allows us to modulate the positional attention map using the box width and height information. Such a design makes it clear that queries in DETR can be implemented as performing soft ROI pooling layer-by-layer in a cascade manner. As a result, it leads to the best performance on MS-COCO benchmark among the DETR-like detection models under the same setting, e.g., AP 45.7\% using ResNet50-DC5 as backbone trained in 50 epochs. We also conducted extensive experiments to confirm our analysis and verify the effectiveness of our methods. This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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The Grounding DINO model was proposed in Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection by Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, Lei Zhang. Grounding DINO extends a closed-set object detection model with a text encoder, enabling open-set object detection. The model achieves remarkable results, such as 52.5 AP on COCO zero-shot. You can use the raw model for zero-shot object detection (the task of detecting things in an image out-of-the-box without labeled data).
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The Grounding DINO model was proposed in Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection by Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, Lei Zhang. Grounding DINO extends a closed-set object detection model with a text encoder, enabling open-set object detection. The model achieves remarkable results, such as 52.5 AP on COCO zero-shot. You can use the raw model for zero-shot object detection (the task of detecting things in an image out-of-the-box without labeled data).
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This model is **Rex-Omni**, a 3B-parameter Multimodal Large Language Model (MLLM) presented in the paper "Detect Anything via Next Point Prediction". It is compatible with the Hugging Face `transformers` library and is licensed under the IDEA License 1.0. > Rex-Omni is a 3B-parameter Multimodal Large Language Model (MLLM) that redefines object detection and a wide range of other visual perception tasks as a simple next-token prediction problem. We provide a series of tutorials to help you get started with Rex-Omni. - Detection Example - Pointing Example - OCR Example - Keypointing Example - Visual Prompting Example - Batch Inference Example
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> We propose Rex-Thinker, a Chain-of-Thought (CoT) reasoning model for object referring that addresses two key challenges: lack of interpretability and inability to reject unmatched expressions. Instead of directly predicting bounding boxes, Rex-Thinker reasons step-by-step over candidate objects to determine which, if any, match a given expression. Rex-Thinker is trained in two stages: supervised fine-tuning to learn structured CoT reasoning, followed by reinforcement learning with GRPO to enhance accuracy, faithfulness, and generalization. Our approach improves both prediction precision and interpretability, while enabling the model to abstain when no suitable object is found. Below is an example of the model's reasoning process: **Rex-Thinker** reformulates object referring as a **Chain-of-Thought (CoT)** reasoning task to improve both interpretability and reliability. The model follows a structured three-stage reasoning paradigm: 2. **Action**: Evaluate each candidate object (obtained via an open-vocabulary detector) against these subgoals using step-by-step reasoning.
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