S100 OpenExplorer

# #Release Introduction

The PTQ conversion sample release includes the model quantization sample package(horizon_model_convert_sample) and the original models used for compilation under the `model_zoo/mapper`.

## #Model Quantization Sample Package: horizon_model_convert_sample

Note

The OE package does not contain the calibration dataset and the original model for the sample by default, you need to execute `00_init.sh` in the corresponding sample folder to get the model and calibration dataset for the current sample.

After the original model and dataset for all samples are acquired, the contents of the model conversion sample package are shown below:

`├── 01_common # This path contains the PTQ sample package generic script and data | ├── calibration_data | ├── data -> /data/ # For storing soft links of the public datasets | ├── model_zoo -> ../../../model_zoo # For storing soft links of the model releases | ├── python | ├── test_data | └── tools ├── 03_classification # Classification model samples ├── 04_detection # Detection model samples ├── 07_segmentation # Segmentation model samples └── data_preprocess.py`

Main contents in the sample package:

Common Algorithm Model Samples are those in the `03_classification/`, `04_detection/`, and `07_segmentation/` folders.

The main goal of these algorithm model samples is to help you:

- experience the model conversion process.
- experience the accuracy evaluation of model conversion.
- experience the model conversion results.

Note

Samples in this section will be updated from time to time to provide sample answers to frequently asked questions.

## #Model Release:`model_zoo`

The `model_zoo` contains two paths: `mapper` and `runtime`.

- The `mapper` path contains the models (floating point models in ONNX or Caffe format) to be used by the PTQ and QAT schemes for model conversion.
- The `runtime` path contains the hbm models that you will use for embedded runtime development.

The next section will introduce you the algorithm model samples.

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