- 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. See System Requirements for more info.
- 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 access to model(s) through secure packaging and execution. Based on KVM Virtual machines and Docker* containers and compatible with the OpenVINO™ Model Server, this new add-on enables packaging for flexible deployment and controlled model access.
- PyPI project moved from openvino-python to openvino, and 2021.1 version to be removed in the default view. The specific version is still available for users depending on this exact version by using openvino-python==2021.1