S100 OpenExplorer

# #The hb_eval_preprocess Tool

The `hb_eval_preprocess` tool is used for preprocessing image data on x86 system.

Preprocessing refers to certain image data operations before being fed into the model, e.g., image resizing, image cropping, image padding, etc.

The basic flow of the hb_eval_preprocess tool to preprocess an image and generate a binary file is shown below:

## #Support Range

The board-side model is only supported by the AIbenchmark PTQ sample model, whose model is included:

mobilenetv1, mobilenetv2, resnet50, googlenet, efficientnet_lite0, efficientnet_lite1, efficientnet_lite2, efficientnet_lite3, efficientnet_lite4, vargconvnet, efficientnasnet_m, efficientnasnet_s, resnet18, yolov2_darknet19, yolov3_darknet53, yolov5x、ssd_mobilenetv1, centernet_resnet101, yolov3_vargdarknet, deeplabv3plus_efficientnetb0, fastscnn_efficientnetb0, deeplabv3plus_efficientnetm1, deeplabv3plus_efficientnetm2.

## #Usage

`Usage: hb_eval_preprocess [OPTIONS] Example: hb_eval_preprocess -m mobilenetv1 -i ./files Options: -h, --help Show this message and exit. --version Show the version and exit. -m, --model_name Input model name. [required] [mobilenetv1|mobilenetv2|resnet50|googlenet|efficientnet_lite0|efficientnet_lite1| efficientnet_lite2|efficientnet_lite3|efficientnet_lite4|vargconvnet|efficientnasnet_m| efficientnasnet_s|resnet18|yolov2_darknet19|yolov3_darknet53|yolov5x|ssd_mobilenetv1| centernet_resnet101|yolov3_vargdarknet|deeplabv3plus_efficientnetb0|fastscnn_efficientnetb0| deeplabv3plus_efficientnetm1|deeplabv3plus_efficientnetm2] -i, --image_dir TEXT Input image dir. [required] -o, --output_dir TEXT Output dir. -v, --val_txt TEXT`

## #Parameters Introduction

Parameter Name | Parameter Description
`-h, --help` | Display help information and exit.
`--version` | Display version information and exit.
`-m, --model_name` | Specify the model name. You can use the `hb_eval_preprocess --help` command to see supported models.
`-i, --image_dir` | Specify the path of input images.
`-o, --output_dir` | Specify the output path.
`-v, --val_txt` | Specify the name of the image file required by the evaluation. The preprocessing will use these names to generate images.

## #Usage Example

Taking the `resnet50` model as an example, you can use the following command to preprocess the image:

`hb_eval_preprocess -m resnet50 -i ./files -o ./output`

## #Output Contents

The `hb_eval_preprocess` tool will generate binary image files in the directory specified by the `--output_dir` parameter.

Hint

More application samples of the `hb_eval_preprocess` tool in the accuracy evaluations of on-board models can be found in Data Pre-process.

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