infer_hf_image_seg

infer_hf_image_seg

About

1.1.1
Apache-2.0

Panoptic segmentation using models from Hugging Face.

Task: Panoptic segmentation
instance
segmentation
inference
transformer
Hugging Face
Pytorch
Dert
resnet
Facebook

This algorithm proposes inference for panoptic segmentation using transformers models from Hugging Face.

LR port panoptic 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.

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 algorithm
algo = wf.add_task(name="infer_hf_image_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.

  • 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 'facebook/detr-resnet-50-panoptic': Name of the model from HF. Other models available: 'facebook/detr-resnet-101-panoptic' and 'facebook/detr-resnet-50-dc5-panoptic'
  • conf_thres (float) - default '0.5': confidence 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 algorithm
algo = wf.add_task(name="infer_hf_image_seg", 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_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_image_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
    Ikomia

License

Apache License 2.0
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