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  • Grounding DINO - NVIDIA Docs - NVIDIA Documentation Hub
    Grounding DINO is an open vocabulary object-detection model included in the TAO Through joint training of text and image data, Grounding DINO is able to accept wide range of text data as input and output the corresponding bounding boxes It supports the following tasks: train evaluate inference export
  • Grounding DINO - GitHub
    Grounding DINO accepts an (image, text) pair as inputs It outputs 900 (by default) object boxes Each box has similarity scores across all input words (as shown in Figures below ) We defaultly choose the boxes whose highest similarities are higher than a box_threshold
  • Enhanced Zero-shot Labeling through the Fusion of DINO and . . .
    Grounded DINO, which is the fusion of DINO with Grounded Pre-training with image, and text pairs It can enable advanced zero shot labeling with text sentence as a prompt
  • Grounding DINO: How to merge Attention on Text and Images
    To operate in genuinely open-set territory, the system needs stronger alignment between language and visual data Without specialized training on image-text pairs and various grounding
  • Simplifying Object Detection: Annotate Your Custom Dataset . . .
    Annotating images for object detection is a fundamental step in training machine learning models to accurately identify and locate objects within images This process involves labeling images
  • Grounding DINO 1. 5: Pushing the Boundaries of Open-Set Object . . .
    Grounding DINO’s architecture consists of an image backbone, a text backbone, a feature enhancer for image-text fusion, a language-guided query selection module, and a cross-modality decoder for refining object boxes
  • Fine-Tuning Grounding DINO: Open-Vocabulary Object Detection
    Grounding DINO fuses language and vision across multiple detector stages Feature Enhancer (Early modality fusion) – The Feature Enhancer stage co-trains vision and language streams via deformable self-attention on image tokens and reciprocal cross-attention with text tokens





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