ascend-tools/dnmetis

1.Install requirements：

pip3.7.5 install python-opencv
cd backend_C++/dnmetis_backend
pip3.7.5 setup.py install

Details of dnmetis_backend installation can be found in backend_C++/dnmetis_backend/README.md. Notice that, you just need to install requirements once for a brand new Ai1-Inference environment。

2.Download model(.om)

1.download efficientnet-b8 model(.om):
URL：baidu pan
Extracted code：tvg0

Original tensorflow model of efficientnet-b8(.pb):
URL：baidu pan
Extracted code：slqm
If you want to acknowledge how to generate om from pb，pls download efficientnet-b8.pb and execute ATC cmd：
.atc --model=MODELDIR/efficientnet−b8.pb −−framework=3 −−inputshape=′images:1,672,672,3′−−output=MODELDIR/efficientnet-b8 --mode=0 --out_nodes='Softmax:0' --soc_version=Ascend310 --input_fp16_nodes=images --output_type=FP16

2.Imagenet-val dataset and labels in val_map.txt:

3.Start execute the inference:

sh run_efficientnet-b8.sh

or

python3.7 main.py --model=./model/efficientnet-b8.om --image_size='672,672,3' --inputs='images:0' --outputs='Softmax:0' --precision=fp16

4.ATC offline model generate (optional):

1.download efficientnet-b8 model(.pb) URL: obs://hwwheel23/efficientnet-b8.pb

2.atc --model=$MODEL_DIR/efficientnet-b8.pb --framework=3 --input_shape='images:1,672,672,3' --output=$MODEL_DIR/efficientnet-b8 --mode=0 --out_nodes='Softmax:0' --soc_version=Ascend310 --input_fp16_nodes=images --output_type=FP16

5.Imagenet2012-val Top1 Accuracy: