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An app for pets, implemented pet detection and recognition.

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ideaRunner/chongaiyoujia

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Android app for real-time pet detection.

This project is developed by TensorFlow android example.

Requisite

Android 5.0 (API 21) or higher is required.

Downloading and Running App

  • Install Android Studio from android.com

  • Clone this repo.

    git clone https://github.com/ideaRunner/chongaiyoujia.git

  • Download trained neural network model from release.

    cd assets
    wget https://github.com/ideaRunner/chongaiyoujia/releases/download/1.0.0/yolov2-tiny-pet_40000.pb
    
  • Open android studio, open this project, follow it's instruction to download those libraries you need. (If you are in Chinese, you will need to setup proxy to download from google)

  • Build (or Rebuild) project.

  • Connect your mobile phone, make sure you have turned on USB debugging mode.

  • Click the green run button on Android Studio, find your connected device and click OK.

  • Wait one minute and the app could be installed on your phone, you can run it to detect 37 classes pets.

Pet Breeds

12 cat and 25 dog breeds from Oxford-IIIT Pet Dataset

  • Cat

    Abyssinian
    Bengal
    Birman
    Bombay
    British_Shorthair
    Egyptian_Mau
    Maine_Coon
    Persian
    Ragdoll
    Russian_Blue
    Siamese
    Sphynx
    
  • Dog

    american_bulldog
    american_pit_bull_terrier
    basset_hound
    beagle
    boxer
    chihuahua
    english_cocker_spaniel
    english_setter
    german_shorthaired
    great_pyrenees
    havanese
    japanese_chin
    keeshond
    leonberger
    miniature_pinscher
    newfoundland
    pomeranian
    pug
    saint_bernard
    samoyed
    scottish_terrier
    shiba_inu
    staffordshire_bull_terrier
    wheaten_terrier
    yorkshire_terrier
    

Generate Your Own App

Train YOLO

For how to train yolo to detect pets or detect your own objects, follow this page.

Convert YOLO model to Tensorflow model

  • Clone and install DarkFlow.

  • Convert. ./flow --model cfg/your-tiny-yolo.cfg --load bin/your-tiny-yolo.weights --savepb --verbalise

    After running the command two files will appear in the ./built_graph directory:

    your-tiny-yolo.meta;
    your-tiny-yolo.pb;
    

Implement trained model

  • Modify DetectorActivity

    private static final String YOLO_MODEL_FILE = "file:///android_asset/your-yolo-model.pb"; private static final DetectorMode MODE = DetectorMode.YOLO;

  • Modify TensorFlowYoloDetector

    Change NUM_CLASSES and LABELS to what you have trained.

Run

After completing the above, you can run and download to your mobile device.

Quick Troubleshooting

Q1: Convert model by darflow AssertionError:

AssertionError: expect 63820056 bytes, found 63820060

Modify the line self.offset = 16 in the ./darkflow/utils/loader.py file and replace with self.offset = 20.