opencv/image_classification_mobilenet

MobileNets

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

MobileNetV2: Inverted Residuals and Linear Bottlenecks

Note:

• image_classification_mobilenetvX_2022apr_int8bq.onnx represents the block-quantized version in int8 precision and is generated using block_quantize.py with block_size=64.

Results of accuracy evaluation with tools/eval.

Models	Top-1 Accuracy	Top-5 Accuracy
MobileNet V1	67.64	87.97
MobileNet V1 block	67.21	87.62
MobileNet V1 quant	55.53	78.74
MobileNet V2	69.44	89.23
MobileNet V2 block	68.66	88.90
MobileNet V2 quant	68.37	88.56

*: 'quant' stands for 'quantized'. **: 'block' stands for 'blockwise quantized'.

Demo

Python

Run the following command to try the demo:

# MobileNet V1
python demo.py --input /path/to/image
# MobileNet V2
python demo.py --input /path/to/image --model v2

# get help regarding various parameters
python demo.py --help

C++

Install latest OpenCV and CMake >= 3.24.0 to get started with:

# A typical and default installation path of OpenCV is /usr/local
cmake -B build -D OPENCV_INSTALLATION_PATH=/path/to/opencv/installation .
cmake --build build

# detect on camera input
./build/opencv_zoo_image_classification_mobilenet
# detect on an image
./build/opencv_zoo_image_classification_mobilenet -m=/path/to/model -i=/path/to/image -v
# get help messages
./build/opencv_zoo_image_classification_mobilenet -h

License

All files in this directory are licensed under Apache 2.0 License.

Reference

• MobileNet V1: https://arxiv.org/abs/1704.04861
• MobileNet V2: https://arxiv.org/abs/1801.04381
• MobileNet V1 weight and scripts for training: https://github.com/wjc852456/pytorch-mobilenet-v1
• MobileNet V2 weight: https://github.com/onnx/models/tree/main/vision/classification/mobilenet