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Grounding DINO model (tiny variant)

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.

drawing

Grounding DINO overview. Taken from the original paper.

Intended uses & limitations

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).

How to use

Here's how to use the model for zero-shot object detection:

import requests

import torch
from PIL import Image
from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection 

model_id = "IDEA-Research/grounding-dino-tiny"
device = "cuda" if torch.cuda.is_available() else "cpu"

processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForZeroShotObjectDetection.from_pretrained(model_id).to(device)

image_url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(image_url, stream=True).raw)
# Check for cats and remote controls
# VERY important: text queries need to be lowercased + end with a dot
text = "a cat. a remote control."

inputs = processor(images=image, text=text, return_tensors="pt").to(device)
with torch.no_grad():
    outputs = model(**inputs)

results = processor.post_process_grounded_object_detection(
    outputs,
    inputs.input_ids,
    box_threshold=0.4,
    text_threshold=0.3,
    target_sizes=[image.size[::-1]]
)

BibTeX entry and citation info

@misc{liu2023grounding,
      title={Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection}, 
      author={Shilong Liu and Zhaoyang Zeng and Tianhe Ren and Feng Li and Hao Zhang and Jie Yang and Chunyuan Li and Jianwei Yang and Hang Su and Jun Zhu and Lei Zhang},
      year={2023},
      eprint={2303.05499},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
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