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:
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.
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
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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/
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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
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.
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.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent