API usage sample for classification task¶
This sample demonstrates the use of the Post-training Optimization Tool API for the task of quantizing a classification model. The MobilenetV2 model from TensorFlow* is used for this purpose. A custom
DataLoader is created to load the ImageNet classification dataset and the implementation of Accuracy at top-1 metric is used for the model evaluation.
How to prepare the data¶
To run this sample, you need to download the validation part of the ImageNet image database and place it in a separate folder, which will be later referred as
<IMAGES_DIR>. Annotations to images should be stored in a separate .txt file (
<IMAGENET_ANNOTATION_FILE>) in the format
How to Run the Sample¶
In the instructions below, the Post-Training Optimization Tool directory
<POT_DIR> is referred to:
<ENV>/lib/python<version>/site-packages/in the case of PyPI installation, where
<ENV>is a Python* environment where OpenVINO is installed and
<version>is a Python* version, e.g.
<INSTALL_DIR>/deployment_tools/tools/post_training_optimization_toolkitin the case of OpenVINO distribution package.
<INSTALL_DIR>is the directory where Intel Distribution of OpenVINO toolkit is installed.
To get started, follow the Installation Guide.
Launch Model Downloader tool to download
mobilenet-v2-1.0-224model from the Open Model Zoo repository.
python3 ./downloader.py --name mobilenet-v2-1.0-224
Launch Model Converter tool to generate Intermediate Representation (IR) files for the model:
python3 ./converter.py --name mobilenet-v2-1.0-224 --mo <PATH_TO_MODEL_OPTIMIZER>/mo.py
Launch the sample script:
python3 <POT_DIR>/compression/api/samples/classification/classification_sample.py -m <PATH_TO_IR_XML> -a <IMAGENET_ANNOTATION_FILE> -d <IMAGES_DIR>
Optional: you can specify .bin file of IR directly using the