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Not Hotdog

Personal implementation of Jin Yang's Not Hotdog app in HBO's Silicon Valley.

App developed with:

• React Native | Flutter | Javascript
• TensorFlow Mobile

Model trained on:

• Yolo v2 Tiny

Links

• Building the Model
• React Native App
• Flutter App
• Javascript PWA

Building the Model

1. Collecting data set

I used COCO dataset 2014 to train the model. COCO dataset contains 80 thing classes, one of which is "hot dog".

I created a Python script (./yolo/coco2yolo.py) to extract all the hot dog images (800+ from train and 400+ from val) and convert the annotations to yolo format.

2. Training Yolov2 Tiny

The model is trained with a single class "hotdog" using this fork of darknet.

• Extract weights from pre-trained yolov2 tiny weights with the original yolov2-tiny.cfg:

darknet.exe partial yolov2-tiny.cfg yolov2-tiny.weights yolov2-tiny.conv.13 13

• Prepare the data files:

  Create obj.data and obj.names files as explained in:

  https://timebutt.github.io/static/how-to-train-yolov2-to-detect-custom-objects/

• Train model on the data set:

The .cfg file and initial weights can be found in ./yolo directory

darknet.exe detector train data\obj.data yolov2-tiny-hotdog.cfg yolov2-tiny.conv.13

3. Converting weights to TensorFlow protobuf (.pb)

The yolo weights is converted to TensorFlow model using darkflow:

flow --model ../yolov2-tiny-hotdog.cfg --load ../yolov2-tiny-hotdog_final.weights  --savepb

The saved .pb file can be found in ./yolo directory.

4. Quantization

The saved .pb is about 44MB. I used the quantization script in Tensorflow repo to quantize and reduced the size to 11MB.

python3 tensorflow/tools/quantization/quantize_graph.py --input=yolov2-tiny-hotdog.pb --output_node_names=output --output=quantized_yolov2-tiny-hotdog.pb --mode=weights

The quantized .pb file can be found in ./react-native-NotHotdog/ios/NotHotdog/data directory.