infer_grounding_dino

The Algorithm proposes a zero-shot object grounding model that can localize objects in an image with a natural language query.

🚀 Use with Ikomia API

1. Install Ikomia API

We strongly recommend using a virtual environment. If you're not sure where to start, we offer a tutorial here.

pip install ikomia

2. Create your workflow

from ikomia.dataprocess.workflow import Workflow
from ikomia.utils.displayIO import display


# Init your workflow
wf = Workflow()    

# Add the Grounding DINO Object Detector
dino = wf.add_task(name="infer_grounding_dino", auto_connect=True)

# Run on your image  
# wf.run_on(path="path/to/your/image.png")
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_dog.png")

# Inspect your results
display(dino.get_image_with_graphics())

☀️ Use with Ikomia Studio

Ikomia Studio offers a friendly UI with the same features as the API.

If you haven't started using Ikomia Studio yet, download and install it from this page.
For additional guidance on getting started with Ikomia Studio, check out this blog post.

📝 Set algorithm parameters

model_name (str) - default 'Swin-T': The GroundingDINO algorithm has two different checkpoint models: ‘Swin-B’ and ‘Swin-T’, with respectively, 172M and 341M of parameters.
prompt (str) - default 'car . person . dog .': Text prompt for the model
conf_thres (float) - default '0.35': Box threshold for the prediction‍
conf_thres_text (float) - default '0.25': Text threshold for the prediction
cuda (bool): If True, CUDA-based inference (GPU). If False, run on CPU

Parameters should be in strings format when added to the dictionary.

from ikomia.dataprocess.workflow import Workflow
from ikomia.utils.displayIO import display

# Init your workflow
wf = Workflow()    

# Add the Grounding DINO Object Detector
dino = wf.add_task(name="infer_grounding_dino", auto_connect=True)

dino.set_parameters({
    "model_name": "Swin-B",
    "prompt": "laptops . smartphone . headphone .",
    "conf_thres": "0.35",
    "conf_thres_text": "0.25"
})

# Run on your image  
# wf.run_on(path="path/to/your/image.png")
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_work.jpg")

# Inspect your results
display(dino.get_image_with_graphics())

🔍 Explore algorithm outputs

Every algorithm produces specific outputs, yet they can be explored them the same way using the Ikomia API. For a more in-depth understanding of managing algorithm outputs, please refer to the documentation.

import ikomia
from ikomia.dataprocess.workflow import Workflow

# Init your workflow
wf = Workflow()

# Add algorithm
algo = wf.add_task(name="infer_grounding_dino", auto_connect=True)

# Run on your image  
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_dog.png")

# Iterate over outputs
for output in algo.get_outputs():
    # Print information
    print(output)
    # Export it to JSON
    output.to_json()

⏩ Advanced usage

Check out the Grounding Dino blog post for more information on this algorithm.

Ikomia-hub/infer_grounding_dino