infer_torchvision_resnext
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infer_torchvision_resnext

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About

1.2.2
BSD-3-Clause

ResNeXt inference model for image classification.

Task: Classification
residual
cnn
classification

ResNext inference model for image classification.

Cat classification

🚀 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_torchvision_resnet", auto_connect=True)

# Run directly on your image
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_cat.jpg")

# Inspect 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.

  • 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 'resnext50': Name of the pre-trained model. Other option 'resnext101"
  • input_size (int) - default '224': Size of the input image.
  • model_weight_file (str, optional): Path to model weights file.
  • class_file (str, , optional): Path to text file (.txt) containing class names. (If using a custom model)

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_resnext", auto_connect=True)
algo.set_parameters({
    "model_name": "resnext101",
    "input_size": "224",
})

# Run directly on your image
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_cat.jpg")

# Inspect 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_resnext", auto_connect=True)

# Run on your image  
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_cat.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

BSD 3-Clause "New" or "Revised" License
Read license full text

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.

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Commercial use

License and copyright notice

Liability

Modification

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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.