MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Results of accuracy evaluation with tools/eval.
Quantization was done via Per Channel method for V1 and Per Tensor for V2
| Models | Top-1 Accuracy | Top-5 Accuracy |
|---|---|---|
| MobileNet V1 | 67.64 | 87.97 |
| MobileNet V1 quant | 40.50 | 53.87 |
| MobileNet V2 | 69.44 | 89.23 |
| MobileNet V2 quant | 58.10 | 87.40 |
*: 'quant' stands for 'quantized'.
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 --helpInstall 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 -hAll files in this directory are licensed under Apache 2.0 License.
- 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