pip install ikomia
About
Mask R-CNN inference model for object detection and segmentation.
Run Mask R-CNN inference model for object detection and segmentation.
🚀 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_mask_rcnn", auto_connect=True)
# Run on your image
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-hub/infer_torchvision_mask_rcnn/main/icons/example.jpg")
# Display result
display(algo.get_image_with_mask_and_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
- model_weight_file (str, default=''): Path to model weights file .pth. If not provided, will use pretrain from torchvision
- class_file (str): Path to class file. Default to coco 2017 classes.
- conf_thres (float, default=0.5): Box threshold for the prediction [0,1]
- iou_thres (float, default=0.5): Intersection over Union, degree of overlap between two boxes. [0,1]
Note: parameter key and value should be in string format when added to the dictionary.
from ikomia.dataprocess.workflow import Workflow
# Init your workflow
wf = Workflow()
# Add algorithm
algo = wf.add_task(name="infer_torchvision_mask_rcnn", auto_connect=True)
algo.set_parameters({
"conf_thres": "0.8",
"iou_thres": "0.8"
})
🔍 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_mask_rcnn", auto_connect=True)
# Run on your image
wf.run_on(url="example_image.png")
# 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.