infer_owl_v2

Run OWLv2 a zero-shot text-conditioned object detection model.

🚀 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 OWLv2 Object Detector
owl = wf.add_task(name="infer_owl_v2", auto_connect=True)

# Run on your image  
# wf.run_on(path="path/to/your/image.png")
wf.run_on(url='http://images.cocodataset.org/val2017/000000039769.jpg')

# Inspect your results
display(owl.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 'google/owlv2-base-patch16-ensemble': The OWLv2 algorithm has different checkpoint models,
  • google/owlv2-base-patch16-ensemble"
  • google/owlv2-base-patch16"
  • google/owlv2-base-patch16-finetuned"
  • google/owlv2-large-patch14"
  • google/owlv2-large-patch14-finetuned"
• prompt (str) - default 'a cat, remote control': Text prompt for the model
• conf_thres (float) - default '0.2': Box threshold for the prediction‍
• cuda (bool): If True, CUDA-based inference (GPU). If False, run on CPU

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


# Init your workflow
wf = Workflow()    

# Add the OWLv2 Object Detector
owl = wf.add_task(name="infer_owl_v2", auto_connect=True)
owl.set_parameters({
    "model_name":"google/owlv2-base-patch16-ensemble",
    "prompt":"a cat, remote control",
    "conf_thres":"0.25",
    "cuda":"True"
})

# Run on your image  
# wf.run_on(path="path/to/your/image.png")
wf.run_on(url='http://images.cocodataset.org/val2017/000000039769.jpg')

# Inspect your results
display(owl.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.

from ikomia.dataprocess.workflow import Workflow

# Init your workflow
wf = Workflow()

# Add algorithm
algo = wf.add_task(name="infer_owl_v2", auto_connect=True)

# Run on your image  
wf.run_on(url='http://images.cocodataset.org/val2017/000000039769.jpg')

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