![]() Requirements Recommended Hardware requirements Pretrained model to generate semantic segmentation labels Model to recognise characters from a preceding OCDNet model Nvidia/tao/pretrained_nvimagenet_backbonesīackbone weights trained on ImageNet to facilitate transfer learning using TAO Model Nameīackbone weights trained on NVImageNet to facilitate transfer learning using TAO The following models are released as part of the TAO Toolkit 5.0. The TAO Toolkit Services Helm chart is hosted on NGC at nvidia/tao/tao-toolkit-api Models Resource to help get started with TAO Toolkit. Nvcr.io/nvidia/tao/tao-toolkit:5.0.0-dataservice Nvcr.io/nvidia/tao/tao-toolkit:5.0.0-deployĬontainer for AI-assisted annotation and few other data services ![]() TensorFlow 2.11.x container for training DNNs TensorFlow 1.15.x container for training DNNs The containers for the TAO Toolkit 5.0 are hosted on NGC under this instance. Here are list of assets as part of TAO 5.0 Containers TAO Toolkit 5.0 version packages containers, models, Jupyter notebooks, start-up script and Helm chart for K8s deployment. ![]() ![]() You can use the power of transfer learning to fine-tune NVIDIA pretrained models with your own data and optimize the model for inference throughput - all without the need for AI expertise or large training datasets. The NVIDIA TAO Toolkit, built on TensorFlow and PyTorch, simplifies and accelerates the model training process by abstracting away the complexity of AI models and the deep learning framework. ![]()
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