Image Classification Demo (Python)¶
Overview¶
The script image_classification.py reads all images and their labels specified in the text file. It then classifies them with ResNet50 model and presents accuracy results.
Download ResNet50 model¶
mkdir -p model/1
wget -P model/1 https://storage.openvinotoolkit.org/repositories/open_model_zoo/2022.1/models_bin/2/resnet50-binary-0001/FP32-INT1/resnet50-binary-0001.bin
wget -P model/1 https://storage.openvinotoolkit.org/repositories/open_model_zoo/2022.1/models_bin/2/resnet50-binary-0001/FP32-INT1/resnet50-binary-0001.xml
Run OpenVINO Model Server¶
docker run -d -v $PWD/model:/models -p 9000:9000 openvino/model_server:latest --model_path /models --model_name resnet --port 9000
Run the client:¶
git clone https://github.com/openvinotoolkit/model_server.git
cd model_server/demos/image_classification/python
python image_classification.py --help
usage: image_classification.py [-h] [--images_list IMAGES_LIST]
[--grpc_address GRPC_ADDRESS]
[--grpc_port GRPC_PORT]
[--input_name INPUT_NAME]
[--output_name OUTPUT_NAME]
[--model_name MODEL_NAME] [--size SIZE]
[--rgb_image RGB_IMAGE]
Arguments¶
Argument |
Description |
|---|---|
-h, –help |
Show help message and exit |
–images_list |
Path to a file with a list of labeled images |
–grpc_address GRPC_ADDRESS |
Specify url to grpc service. Default:localhost |
–grpc_port GRPC_PORT |
Specify port to grpc service. Default: 9000 |
–input_name |
Specify input tensor name. Default: input |
–output_name |
Specify output name. Default: resnet_v1_50/predictions/Reshape_1 |
–model_name |
Define model name, must be same as is in service. Default: resnet |
–size SIZE |
The size of the image in the model |
–rgb_image RGB_IMAGE |
Convert BGR channels to RGB channels in the input image |
Usage example¶
python image_classification.py --grpc_port 9000 --input_name 0 --output_name 1463 --images_list ../input_images.txt
Start processing:
Model name: resnet
Images list file: ../input_images.txt
../../common/static/images/airliner.jpeg (1, 3, 224, 224) ; data range: 0.0 : 255.0
Processing time: 25.08 ms; speed 39.87 fps
1 airliner 404 ; Correct match.
../../common/static/images/arctic-fox.jpeg (1, 3, 224, 224) ; data range: 0.0 : 255.0
Processing time: 22.97 ms; speed 43.53 fps
2 Arctic fox, white fox, Alopex lagopus 279 ; Correct match.
../../common/static/images/bee.jpeg (1, 3, 224, 224) ; data range: 0.0 : 255.0
Processing time: 24.45 ms; speed 40.90 fps
3 bee 309 ; Correct match.
../../common/static/images/golden_retriever.jpeg (1, 3, 224, 224) ; data range: 0.0 : 255.0
Processing time: 23.93 ms; speed 41.78 fps
4 golden retriever 207 ; Correct match.
../../common/static/images/gorilla.jpeg (1, 3, 224, 224) ; data range: 0.0 : 255.0
Processing time: 24.72 ms; speed 40.46 fps
5 gorilla, Gorilla gorilla 366 ; Correct match.
../../common/static/images/magnetic_compass.jpeg (1, 3, 224, 224) ; data range: 0.0 : 247.0
Processing time: 24.74 ms; speed 40.43 fps
6 magnetic compass 635 ; Correct match.
../../common/static/images/peacock.jpeg (1, 3, 224, 224) ; data range: 0.0 : 255.0
Processing time: 22.39 ms; speed 44.66 fps
7 peacock 84 ; Correct match.
../../common/static/images/pelican.jpeg (1, 3, 224, 224) ; data range: 0.0 : 255.0
Processing time: 25.96 ms; speed 38.53 fps
8 pelican 144 ; Correct match.
../../common/static/images/snail.jpeg (1, 3, 224, 224) ; data range: 0.0 : 248.0
Processing time: 23.68 ms; speed 42.23 fps
9 snail 113 ; Correct match.
../../common/static/images/zebra.jpeg (1, 3, 224, 224) ; data range: 0.0 : 255.0
Processing time: 23.68 ms; speed 42.24 fps
10 zebra 340 ; Correct match.
Overall accuracy= 100.0 %
Average latency= 23.5 ms