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Yolov5 Inference for Tensorflow

This repository provides a script and recipe to Inference of the Yolov5 model.

Notice

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.

Quick Start Guide

1. Clone the respository

git clone https://gitee.com/ascend/ModelZoo-TensorFlow.git
cd Modelzoo-TensorFlow/ACL_TensorFlow/contrib/cv/Yolov5_for_ACL

2. Download and preprocess the dataset

1. Refer to this url to download and preprocess the dataset The operation is as follows:

# run script in /script/get_coco_dataset_2017.sh to download COCO 2017 Dataset
# preprocess coco dataset
cd data
mkdir dataset
cd ..
cd scripts
python coco_convert.py --input ./coco/annotations/instances_val2017.json --output val2017.pkl
python coco_annotation.py --coco_path ./coco 
python img2bin.py --img-dir ./coco/images --bin-dir ./coco/input_bins
mv coco ..

There will generate coco2017 test data set under data/dataset/.

3. Offline Inference

Convert pb to om.

• configure the env

  Please follow the guide to set the envs

• convert pb to om

  atc --model=yolov5_tf2_gpu.pb --framework=3 --output=yolov5_tf2_gpu --soc_version=Ascend310 --input_shape="Input:1,640,640,3" --out_nodes="Identity:0;Identity_1:0;Identity_2:0;Identity_3:0;Identity_4:0;Identity_5:0" --log=info
  

• Build the program

  bash build.sh
  

• Run the program:

  cd offline_inference
  bash benchmark_tf.sh
  

• Run the post process:

  cd ..
  python3 offline_inference/postprocess.py
  

Performance

Result

Our result were obtained by running the applicable inference script. To achieve the same results, follow the steps in the Quick Start Guide.

Inference accuracy results

Reference

[1] https://github.com/hunglc007/tensorflow-yolov4-tflite

[2] https://github.com/ultralytics/yolov5

[3]https://github.com/khoadinh44/YOLOv5_customized_data