NEW RELEASE: Intel® Distribution of OpenVINO™ toolkit 2021.2 Adds RHEL 8.2 Support, New OpenVINO™ Security Add-on for model encryption, and New Pre-trained Models
Enhanced development experience with newly integrated Deep Learning Workbench with the Intel® DevCloud, RHEL 8.2 support, OpenVINO™ Security Add-on for model encryption, and new pre-trained models
- Integrates the Deep Learning Workbench with the Intel® DevCloud for the Edge as a Beta release. Graphically analyze models using the Deep Learning Workbench on the Intel® DevCloud for the Edge (instead of a local machine only) to compare, visualize and fine-tune a solution against multiple remote hardware configurations.
- Introduces support for Red Hat Enterprise Linux (RHEL) 8.2.
- Introduces per-channel quantization support in the Model Optimizer for models quantized with TensorFlow Quantization-Aware Training containing per-channel quantization for weights, which improves performance by model compression and latency reduction.
- Pre-trained models and support for public models to streamline development:
- Public Models: Yolov4 (for object detection), AISpeech (for speech recognition), and DeepLabv3 (for semantic segmentation)
- Pre-trained Models: Human Pose Estimation (update), Formula Recognition Polynomial Handwritten (new), Machine Translation (update), Common Sign Language Recognition (New), and Text-to-Speech (new)
- New OpenVINO™ Security Add-on, which controls of models through secure packaging and model execution. Based on Docker* containers and is compatible with the OpenVINO™ Model Server for a scalable serving microservice, this new add-on enables packaging for flexible deployment and security.
OpenVINO include Open Model Zoo component, which include a set of models intended to solve various tasks, please look through relevant OpenVINO documentation
Note, there was no model with name person-detection-0003, might be you mean person-detection-0203 or person-detection-retail-0013?