A tensorflow implementation of fire-FRD-CNN network.

To training the model, the prerequisites are:

1. python 2.7
2. easydict
3. joblib
4. numpy
5. opencv-python
6. Pillow
7. tensorflow-gpu

Please follow the belowing steps to training the model.

• Download KITTI object detection dataset. Put them under $fire-FRD-CNN_ROOT/data/KITTI/. Unzip them, then you will get two directories: $fire-FRD-CNN_ROOT/data/KITTI/training/ and $fire-FRD-CNN_ROOT/data/KITTI/testing/.

• Now we need to split the training data into a training set and a vlidation set.

  cd $fire-FRD-CNN_ROOT/data/KITTI/
  mkdir ImageSets
  cd ./ImageSets
  ls ../training/image_2/ | grep ".png" | sed s/.png// > trainval.txt

  trainval.txt contains indices to all the images in the training data. In our experiments, we randomly split half of indices in trainval.txt into train.txt to form a training set and rest of them into val.txt to form a validation set. For your convenience, we provide a script to split the train-val set automatically. Simply run

  cd $fire-FRD-CNN_ROOT/data/
  python random_split_train_val.py

  then you should get the train.txt and val.txt under $fire-FRD-CNN_ROOT/data/KITTI/ImageSets.

  When above two steps are finished, the structure of $fire-FRD-CNN_ROOT/data/KITTI/ should at least contain:

  $SQDT_ROOT/data/KITTI/
                    |->training/
                    |     |-> image_2/00****.png
                    |     L-> label_2/00****.txt
                    |->testing/
                    |     L-> image_2/00****.png
                    L->ImageSets/
                          |-> trainval.txt
                          |-> train.txt
                          L-> val.txt

• Next, download the CNN model pretrained for ImageNet classification:

  cd $fire-FRD-CNN_ROOT/data/
  # SqueezeNet
  wget https://www.dropbox.com/s/fzvtkc42hu3xw47/SqueezeNet.tgz
  tar -xzvf SqueezeNet.tgz
  

• Now we can start training. Training script can be found in $fire-FRD-CNN_ROOT/scripts/train.sh, which contains commands to train the model: fire-FRD-CNN

  cd $fire-FRD-CNN_ROOT/
  ./scripts/train.sh -net fire-FRD-CNN -train_dir /home/scott/logs/fire-FRD-CNN -gpu 0

  Training logs are saved to the directory specified by -train_dir. GPU id is specified by -gpu. Network to train is specificed by -net

• Before evaluation, you need to first compile the official evaluation script of KITTI dataset

  cd $fire-FRD-CNN/src/dataset/kitti-eval
  make

• Then, you can launch the evaluation script (in parallel with training) by

  cd $fire-FRD-CNN/
  ./scripts/eval.sh -net fire-FRD-CNN -eval_dir /home/scott/logs/fire-FRD-CNN -image_set (train|val) -gpu 1