#Common Algorithm Model Samples

#Sample Location

The common algorithmic model samples are located at 03_classification/, 04_detection/ and 07_segmentation/ folders in the horizon_model_convert_sample path.

#Prepare Datasets

#Dataset Download Address

The dataset can be downloaded from the following address.

Dataset	Address
ImageNet	https://www.image-net.org/download.php
COCO	https://cocodataset.org/
VOC	http://host.robots.ox.ac.uk/pascal/VOC/ (need to download both versions 2007 and 2012)
Cityscapes	https://github.com/mcordts/cityscapesScripts

#Dataset Reference Structure

To facilitate your subsequent steps, after the dataset has been downloaded, you need to process the evaluation dataset according to the structure suggested by Horizon below.

Dataset	Description
ImageNet Dataset	We recommend that you unzip the downloaded dataset into the following structure. Please contact Horizon if you encounter any problems during data preparation. imagenet/ ├── calibration_data │ ├── ILSVRC2012_val_00000001.JPEG │ ├── ... │ └── ILSVRC2012_val_00000100.JPEG ├── ILSVRC2017_val.txt ├── val │ ├── ILSVRC2012_val_00000001.JPEG │ ├── ... │ └── ILSVRC2012_val_00050000.JPEG └── val.txt
COCO Dataset	We recommend that you unzip the downloaded dataset into the following structure. Please contact Horizon if you encounter any problems during data preparation. coco/ ├── calibration_data │ ├── COCO_val2014_000000181007.jpg │ ├── ... │ └── COCO_val2014_000000181739.jpg └── coco_val2017 ├── annotations │ ├── instances_train2017.json │ └── instances_val2017.json └── images ├── 000000000139.jpg ├── 000000000285.jpg ├── ... ├── 000000581615.jpg └── 000000581781.jpg
VOC Dataset	Attention Please note that the VOC2012 directory currently stores two datasets, VOC2007 and VOC2012, so please follow the directory structure below for the evaluation dataset. Please contact Horizon if you encounter any problems during data preparation. VOCdevkit/ └── VOC2012 ├── Annotations │ ├── 2007_000027.xml │ ├── ... │ └── 2012_004331.xml ├── ImageSets │ ├── Action │ │ ├── jumping_train.txt │ │ ├── jumping_trainval.txt │ │ ├── jumping_val.txt │ │ ├── ... │ │ ├── val.txt │ │ ├── walking_train.txt │ │ ├── walking_trainval.txt │ │ └── walking_val.txt │ ├── Layout │ │ ├── train.txt │ │ ├── trainval.txt │ │ └── val.txt │ ├── Main │ │ ├── aeroplane_train.txt │ │ ├── aeroplane_trainval.txt │ │ ├── aeroplane_val.txt │ │ ├── ... │ │ ├── train.txt │ │ ├── train_val.txt │ │ ├── trainval.txt │ │ ├── tvmonitor_train.txt │ │ ├── tvmonitor_trainval.txt │ │ ├── tvmonitor_val.txt │ │ └── val.txt │ └── Segmentation │ ├── train.txt │ ├── trainval.txt │ └── val.txt ├── JPEGImages │ ├── 2007_000027.jpg │ ├── ... │ └── 2012_004331.jpg ├── SegmentationClass │ ├── 2007_000032.png │ ├── ... │ └── 2011_003271.png ├── SegmentationObject │ ├── 2007_000032.png │ ├── ... │ └── 2011_003271.png └── train.txt
Cityscapes Dataset	We recommend that you unzip the downloaded dataset into the following structure. Please contact Horizon if you encounter any problems during data preparation. cityscapes/ ├── cityscapes_calibration_data │ ├── aachen_000000_000019_leftImg8bit.png │ ├── ... │ └── aachen_000099_000019_leftImg8bit.png ├── gtFine │ ├── test │ │ ├── berlin │ │ ├── ... │ │ └── munich │ ├── train │ │ ├── aachen │ │ ├── ... │ │ └── zurich │ └── val │ ├── frankfurt │ ├── lindau │ └── munster ├── leftImg8bit │ ├── test │ │ ├── berlin │ │ ├── ... │ │ └── munich │ ├── train │ │ ├── aachen │ │ ├── ... │ │ └── zurich │ └── val │ ├── frankfurt │ ├── lindau │ └── munster ├── license.txt └── README

#Prepare Models

When using the model conversion sample package, please prepare the corresponding floating-point model first.

#Demo Of Algorithm Model Samples

Taking the Resnet50 model as an example, this section illustrates the steps of floating-point to fixed-point model conversion by using the scripts in 03_classification/03_resnet50/.

#Docker Container Prep

First complete the docker installation and configuration and enter the docker container accoridng to Docker Container Deployment .

#Obtain The Original Model And Calibration Dataset

Execute 00_init.sh in the 03_classification/03_resnet50/ folder to obtain the model and calibration dataset needed for the current sample.

#Check If the Model is Executable

Run the script as follows:

#Prepare Calibration Dataset

Run the 02_preprocess.sh script in the same directory, as follows:

#Building Models

Run the 03_build.sh script in the same directory, as follows:

The output of the above script is as follows:

#Single Image Inference

Run the 04_inference.sh script to inference a single image, as follows:

#Accuracy Evaluation

Run the 05_evaluate.sh script to evaluate the accuracy, as follows:

#FAQ

#Why is the reproduced accuracy slightly different from the indicators in the documentation?

There are two possible reasons:

1. There may be minor differences in calculation methods when in different server environments, which can cause small data fluctuations in the accuracy of the compiled fixed-point ONNX models in different server environments when compared with the documentation.
2. The different versions of third-party libraries such as OpenCV and NumPy, which may produce different results after pre-processing, and this may also lead to slight data fluctuations in reproduced accuracy when compared with the documentation.

No need to worry much about this situation, the records provided in the documentation is only for reference, and it is ok that your reproduced accuracy is slightly different from those in documents.

#Why is the fixed-point model accuracy not aligned with the on-board accuracy of the hbm file in the ai_benchmark sample?

In the standard delivery, when adding the sample, we have already aligned the fixed-point model accuracy with the hbm on-board accuracy in the ai_benchmark sample.

If you find any unaligned accuracy, we recommend you first checking the model input for consistency.

When executing the fixed-point model evaluation script, you use the dataset of image type, while for the hbm model used on board, you use the binary dataset converted by the hb_eval_preprocess tool.

Based on this, if the dataset you used on-board is not generated by using the above methods, we recommend that you first use our data preprocessing tool (i.e., hb_eval_preprocess) to regenerate the dataset needed for on-board running on the same server that you run the fixed-point model accuracy and rerun the on-board accuracy to ensure the model inputs are consistent.