TachibanaYoshino / State-Farm-Distracted-Driver-Detect-Pytorch

This project is from a kaggle competition in 2016, we use mobileNet V2 to achieve classification recognition based on pytorch.

State-Farm-Distracted-Driver-Detect-Pytorch

This project is from a kaggle competition in 2016, we use mobileNet V2 to achieve classification recognition based on pytorch.

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1、Brief

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1、1 project source

The implementation of the [State Farm Distracted Driver Detection] competition in kaggle.

1、2 Task content

Classify a picture of driver behavior, a total of 10 categories
The 10 classes to predict are:

c0: safe driving
c1: texting - right
c2: talking on the phone - right
c3: texting - left
c4: talking on the phone - left
c5: operating the radio
c6: drinking
c7: reaching behind
c8: hair and makeup
c9: talking to passenger

1、3 Method used

Training and identification with MobileNet V2

1、4 Dataset download address:

https://www.kaggle.com/c/state-farm-distracted-driver-detection/data

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2、Project configuration

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2、1 Operating environment

1: Download the repository to the local;
2: In this directory, create a folder as follows, and download the train dataset to the data folder:

---data
------train

2、2 Python dependency package

pytorch1.0.1
easydict
torchvision
PIL
python-opencv
pip install --upgrade git+https://github.com/Lyken17/pytorch-OpCounter.git

2、3 Running

2、3、1 training

• python train.py

2、3、2 testing

• Select the model to load, write it to test.py, then python test.py