amlnn-model-playground/examples/mobilenet

Model Description

This model is converted from MobileNetV2 pretrained weights originally released by Google under the Apache License 2.0.

Original model:

• Architecture: MobileNetV2
• Source: TensorFlow / Keras official implementation

The model has been converted and optimized into ADLA format for deployment on Amlogic NPU platforms.

Demo Run

CPP

1. Compile

Prerequisites:

• Android NDK (r25e recommended)
• ANDROID_NDK_PATH environment variable set

Build:

# Build for arm64-v8a
cd examples/mobilenet/cpp
AMLNN_HOME=/path/to/amlnn-toolkit ./build-android.sh -a arm64-v8a

The executable will be generated at build/android/mobilenet_v2_demo (Note: executable name may vary, verify in build folder).

2. Run

# Push executable to device
adb push build/android/mobilenet_v2_demo /data/local/tmp/
adb push model/mobilenet_v2_1.0_224_quant_A311D2.adla /data/local/tmp/
adb push model/cat_224x224.jpg /data/local/tmp/
adb push model/labels.txt /data/local/tmp/

# Run on device
adb shell
cd /data/local/tmp
chmod +x mobilenet_v2_demo
export LD_LIBRARY_PATH=/vendor/lib64 or (/vendor/lib)

# Usage: ./mobilenet_v2_demo <model_path> <image_path> <labels_path>
./mobilenet_v2_demo mobilenet_v2_1.0_224_quant_A311D2.adla cat_224x224.jpg labels.txt

Note: Replace mobilenet_v2_1.0_224_quant_A311D2.adla with your actual model file path.

Python

Prerequisites:

• Python 3.10
• Required packages: numpy, Pillow, amlnnlite

Install dependencies:

pip install numpy Pillow amlnnlite-1.0.0-cp310-cp310-linux_aarch64.whl

Run on device:

# Basic usage
python mobilenetv2.py --model-path ./mobilenet_v2_1.0_224_quant_A311D2.adla

# Run with performance testing (100 cycles)
python mobilenetv2.py --model-path ./mobilenet_v2_1.0_224_quant_A311D2.adla --run-cycles 100

The script will automatically process all image files (.jpg, .jpeg, .png, .bmp) in the current directory and display top-5 classification results for each image.

Results

The program will print the top-5 classification results with probabilities for each processed image.

Example output:

# python demo result
============================================================
Processing image 1/3: dog_224x224.jpg
============================================================

Top-5 Classification Results:
  1. Shih-Tzu                  (probability: 0.9239)
  2. Pekinese                  (probability: 0.0476)
  3. Lhasa                     (probability: 0.0263)
  4. Brabancon griffon         (probability: 0.0004)
  5. Dandie Dinmont            (probability: 0.0003)

============================================================
Processing image 2/3: cat_224x224.jpg
============================================================

Top-5 Classification Results:
  1. tiger cat                 (probability: 0.4774)
  2. tabby                     (probability: 0.4324)
  3. Egyptian cat              (probability: 0.0542)
  4. lynx                      (probability: 0.0150)
  5. Persian cat               (probability: 0.0025)

============================================================
Processing image 3/3: fish_224x224.jpeg
============================================================

Top-5 Classification Results:
  1. goldfish                  (probability: 0.9998)
  2. conch                     (probability: 0.0001)
  3. trifle                    (probability: 0.0000)
  4. axolotl                   (probability: 0.0000)
  5. American lobster          (probability: 0.0000)

The classification results show the model's confidence scores (probabilities) for each detected class, with the highest probability indicating the most likely classification.