IMAGE_PATH = "weights/dog-3.jpeg" TEXT_PROMPT = "chair. inference import load_model, load_image, predict, annotate import cv2 model = load_model( "groundingdino/config/GroundingDINO_SwinT_OGC.py", "weights/groundingdino_swint_ogc.pth") It will be compiled under CPU-only mode if no CUDA available.Ĭlone the GroundingDINO repository from GitHub.įrom groundingdino.
If you have a CUDA environment, please make sure the environment variable CUDA_HOME is set.
Grounding DINO with Stable Diffusion and GLIGEN demos. We suggest separating different category names with. The number of words in a sentence may not equal to the number of text tokens. 
Note that each word can be split to more than one tokens with different tokenlizers. If you want to obtain objects of specific phrases, like the dogs in the sentence two dogs with a stick., you can select the boxes with highest text similarities with dogs as final outputs. We extract the words whose similarities are higher than the text_threshold as predicted labels. We defaultly choose the boxes whose highest similarities are higher than a box_threshold. Each box has similarity scores across all input words. It outputs 900 (by default) object boxes. Grounding DINO accepts an (image, text) pair as inputs. Marrying Grounding DINO and GLIGEN ⭐ Explanations/Tips for Grounding DINO Inputs and Outputs 5: A demo for Grounding DINO is available at Colab. Now the model can run on machines without GPUs. 8: A YouTube video about Grounding DINO and basic object detection prompt engineering. 6: We build a new demo by marrying GroundingDINO with Segment-Anything named Grounded-Segment-Anything aims to support segmentation in GroundingDINO. 8: We release demos to combine Grounding DINO with Stable Diffusion for image editings. 8: We release demos to combine Grounding DINO with GLIGEN for more controllable image editings. 5: Refer to CV in the Wild Readings for those who are interested in open-set recognition!. Collaboration with Stable Diffusion for Image Editting. COCO zero-shot 52.5 AP (training without COCO data!). LLaVA: Large Language and Vision Assistant. GLIGEN: Open-Set Grounded Text-to-Image Generation. X-GPT: Conversational Visual Agent supported by X-Decoder. SEEM: Segment Everything Everywhere All at Once. OpenSeeD: A Simple and Strong Openset Segmentation Model. 
Grounding DINO with GLIGEN for Controllable Image Editing. Grounded-SAM: Marrying Grounding DINO with Segment Anything. DetGPT: Detect What You Need via Reasoning. Official PyTorch implementation of "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection": the SoTA open-set object detector.
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