pip install ikomia
About
1.1.3
Apache-2.0
Instance segmentation using models from Hugging Face.
This algorithm proposes inference for instance segmentation using transformers models from Hugging Face.
🚀 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_hf_instance_seg", auto_connect=True)
# Run on your image
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_LR.jpg")
# Inpect your 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_name (str) - default "facebook/maskformer-swin-base-coco": Name of the model. More models 'facebook/maskeformer' available on HF.
- conf_thres (float) - default '0.5': The probability score threshold to keep predicted instance masks.
- conf_mask_thres (float) - default '0.5': T Threshold to use when turning the predicted masks into binary values.
- conf_overlap_mask_area_thres (float) - default '0.8': The overlap mask area threshold to merge or discard small disconnected parts within each binary instance mask.
- 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 algorithm
algo = wf.add_task(name="infer_hf_instance_seg", auto_connect=True)
algo.set_parameters({
'model_name': 'facebook/maskformer-swin-base-coco',
'conf_thres': '0.5',
"conf_mask_thres": "0.5",
"conf_overlap_mask_area_thres": "0.8",
'cuda': 'True',
})
# Run on your image
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_LR.jpg")
# Inpect your result
display(algo.get_image_with_mask_and_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_hf_instance_seg", auto_connect=True)
# Run on your image
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_LR.jpg")
# Iterate over outputs
for output in algo.get_outputs():
# Print information
print(output)
# Export it to JSON
output.to_json()
Developer
Ikomia
License
Apache License 2.0
A permissive license whose main conditions require preservation of copyright and license notices. Contributors provide an express grant of patent rights. Licensed works, modifications, and larger works may be distributed under different terms and without source code.
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