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This repository provides a script and recipe to Inference of the SegdecNet model.
This sample only provides reference for you to learn the Ascend software stack and is not for commercial purposes.
Before starting, please pay attention to the following adaptation conditions. If they do not match, may leading in failure.
Conditions | Need |
---|---|
CANN Version | >=5.0.3 |
Chip Platform | Ascend310/Ascend310P3 |
3rd Party Requirements | Please follow the 'requirements.txt' |
git clone https://gitee.com/ascend/ModelZoo-TensorFlow.git
cd Modelzoo-TensorFlow/ACL_TensorFlow/contrib/cv/SegdecNet_for_ACL
Download the KolektorSDD Validation dataset by yourself.
You should split the dataset into three folds to perform 3-fold cross validation.split
Images Preprocess:
cd scripts
bash run_preprocess.sh
The images bin files is stored in output/images/ The labels bin files is stored in output/labels/
Convert pb to om.
configure the env
Please follow the guide to set the envs
convert pb to om
atc --model=./output/SEGDEC-NET_tf.pb --framework=3 --output=./output/SEGDEC-NET_tf --output_type=FP32 --soc_version=Ascend310 --input_shape="images:1,1408,512,1" --log=info
Build the program
cd ../
bash build.sh
Run the program:
cd scripts
bash benchmark_tf.sh
Postprocess:
bash run_postprocess.sh
Our result was obtained by running the applicable inference script. To achieve the same results, follow the steps in the Quick Start Guide.
model | AP of CPU | AP of NPU |
---|---|---|
SegdecNet | 0.9536 | 0.9528 |
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