#Foreword

Horizon OpenExplorer currently provides 2 sets of model quantization schemes at the same time.

• PTQ: Post-training Quantization.
• QAT: Quantization Awareness Training, which only supports the Pytorch framework for now.

Among them:

• Both solutions do not interfere with the training phase of the floating-point model, which is your own responsibility. Horizon has also provided some open-source implementations of the public version of Pytorch for efficient models in classification, detection, segmentation, and other scenarios in the samples/ai_toolchain/horizon_model_train_sample for reference, with the support for training and replication on the host.

• For PTQ scheme, you need to quantize the model in the host development environment, and then copy the compiled .hbm model to the dev board environment for subsequent deployment.

• For the QAT scheme, you need to complete the QAT training of the model in the host development environment, perform the quantization conversion, and then copy the compiled .hbm model to the dev board environment for subsequent deployment.

For both of the above quantization schemes and the development environment of the efficient model, Horizon provides both local manual installation and Docker containers. We strongly recommend using Docker containers as they do not pollute the local environment and is easy to use.

We will introduce you to the development and running environment requirements and deployment process required for using the toolchain in two parts: Pre-installation Preparation and Software Installation.