pip install ikomia
About
Training process for MMOCR from MMLAB in text recognition
Training process for MMOCR from MMLAB in text recognition.You can choose a predefined model configuration from MMLAB's model zoo or use custom models and custom pretrained model_weight_file by ticking Expert mode button.
🚀 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
To try this code snippet, you can download and extract from wildreceipt. Then make sure you fill the parameter dataset_folder correctly.
from ikomia.dataprocess.workflow import Workflow
# Init your workflow
wf = Workflow()
# Add dataset
dataset = wf.add_task(name="dataset_wildreceipt", auto_connect=False)
# Set dataset parameters
dataset.set_parameters({'dataset_folder': 'dataset/folder'})
# Add algorithm
algo = wf.add_task(name="train_mmlab_text_recognition", auto_connect=True)
# Run training
wf.run()
📝 Set algorithm parameters
- dataset_folder (str, default=""): path to the dataset folder.
Note: parameter key and value should be in string format when added to the dictionary.
☀️ 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.
🔍 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.
from ikomia.dataprocess.workflow import Workflow
# Init your workflow
wf = Workflow()
# Add algorithm
dataset = wf.add_task(name="train_mmlab_text_recognition", auto_connect=False)
# Set dataset parameters
dataset.set_parameters({'dataset_folder': 'dataset/folder'})
# Load dataset
wf.run()
# Look at the loaded data
print(dataset.get_output(0).data)
Developer
Ikomia
License
Apache License 2.0
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