train_mmlab_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.

pip install ikomia

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.

• 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.

🔍 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)