Triplet Loss & Batch triplet loss & SVM

Here, I have some experiments in order to make more accurate classifier. Triplet Loss, Batch Triplet, SVM are used for experiments. softmax features + SVM was the best in my result as a record of 91% top1 accuracy.

Dataset

Kaggle’s State Farm Distracted Driver Detection Dataset is used for evaluating the performance of Image Classification, you should modify sampledata.py to fit your dataset.

Setup

Rebuild your caffe directory:

cd $CAFFEROOT$
cp Makefile.configexample Makefile.config

Remember to uncomment the line to makesure your python layers could be found:

WITH_PYTHON_LAYER := 1

Then build caffe and pycaffe:

make all -j8 & make pycaffe

Usage

1. Modify sampledata.py, config.py and train.py to fit your dataset and working environment.

2. Pre-train your model with softmax loss.

3. Finetune triplet model based on your pre-trained model.

4. Learn to adjust parameters.

Result

softmax

triplet loss

batch triplet loss

softmax+svm

Reference

This project is based on hizhangp’s job