pip install ikomia
About
1.3.2
BSD-3-Clause
Faster R-CNN inference model for object detection.
Run Faster R-CNN inference model for object detection.
🚀 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.
2. Create your workflow
from ikomia.dataprocess.workflow import Workflow
from ikomia.utils.displayIO import display
# Init your workflow
wf = Workflow()
# Add algorithm
algo = wf.add_task(name="infer_torchvision_faster_rcnn", auto_connect=True)
# Run on your image
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_work.jpg")
# Inpect your result
display(algo.get_image_with_graphics())
☀️ Use with Ikomia Studio
Ikomia Studio offers a friendly UI with the same features as the API.
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If you haven't started using Ikomia Studio yet, download and install it from this page.
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For additional guidance on getting started with Ikomia Studio, check out this blog post.
📝 Set algorithm parameters
By default the algorithm will use the FasterRcnn model trained on the COCO 2017 dataset.
- conf_thres (float) default '0.5': Box threshold for the prediction [0,1]
- model_weight_file (str, optional): Path to model weights file.
- class_file (str, optional) = Path to text file (.txt) containing class names.
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 algorithm
algo = wf.add_task(name="infer_torchvision_faster_rcnn", auto_connect=True)
algo.set_parameters({
"conf_thres": "0.5",
})
# Run on your image
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_work.jpg")
# Inpect your result
display(algo.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_torchvision_faster_rcnn", auto_connect=True)
# Run on your image
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_work.jpg")
# Iterate over outputs
for output in algo.get_outputs():
# Print information
print(output)
# Export it to JSON
output.to_json()
Developer
Ikomia
License
BSD 3-Clause "New" or "Revised" License
A permissive license similar to the BSD 2-Clause License, but with a 3rd clause that prohibits others from using the name of the copyright holder or its contributors to promote derived products without written consent.
| Permissions | Conditions | Limitations |
|---|---|---|
Commercial use | License and copyright notice | Liability |
Modification | Warranty | |
Distribution | ||
Private use |
This is not legal advice: this description is for informational purposes only and does not constitute the license itself. Provided by choosealicense.com.