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

AMLNN SDK Setup

Resolve the AMLNN nnsdk dependency using one of the following methods:

• Priority 1 – Environment variable (recommended)

  export AMLNN_HOME=/path/to/amlnn-toolkit
  

• Priority 3 – Sibling directory fallback (automatic) Place amlnn-toolkit as a sibling to amlnn-model-playground:

  git clone git@github.com:Amlogic-NN/amlnn-toolkit.git ../amlnn-toolkit
  

Prerequisites:

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

Build:

# Build for arm64-v8a
cd examples/mobilenet/cpp
./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.