tao-train-ocrnet

You can train an OCRNet model by running the `tao-train-ocrnet train` command with your specified input images and ground truth files. This automates the scene text recognition training process using a predefined configuration without requiring extensive Python programming.

Running OCRNet scene text recognition model training and inference requires Docker and the NVIDIA container toolkit to be installed in your environment. These dependencies are necessary to execute the model management workflows.

Yes, you can evaluate trained OCRNet models against a separate test dataset. This functionality allows you to measure the accuracy and performance of your scene text recognition model before moving to deployment.

Yes, the Skill supports deploying trained OCRNet models for inference. This enables you to transition your scene text recognition model from evaluation to real-time optical character recognition capabilities within an application or service.

The best way to manage the OCRNet model lifecycle is using this Skill to streamline training, evaluation, and deployment. It allows data scientists to efficiently handle image-based text recognition tasks from data preprocessing to inference without manual Python coding.