1. Requirements
2. Basic installation
3. Usage
4. Results
5. Evaluation
6. GUI

If you have any questions with this repository, please feel free to contact me at zhucz13@mails.tsinghua.edu.cn

1. Requirements for Caffe and pycaffe (see: Caffe installation instructions)

Note: Caffe must be built with support for Python layers!

# In your Makefile.config, make sure to have this line uncommented
WITH_PYTHON_LAYER := 1
# Unrelatedly, it's also recommended that you use CUDNN
USE_CUDNN := 1

2. Python packages you might not have: cython, python-opencv, easydict
3. [Optional] MATLAB is required for official PASCAL VOC evaluation only. The code now includes unofficial Python evaluation code.

1. Clone the Kitti repository

# Make sure to clone with --recursive
git clone --recursive https://github.com/czhu95/kitti.git
cd kitti
git checkout master

2. We'll call the directory that you cloned KITTI_ROOT

   Ignore notes 1 and 2 if you followed step 1 above.

   Note 1: If you didn't clone KITTI with the --recursive flag, then you'll need to manually clone the caffe-fast-rcnn submodule:

   git submodule update --init --recursive

   Note 2: The caffe-fast-rcnn submodule needs to be on the faster-rcnn branch (or equivalent detached state). This will happen automatically if you followed step 1 instructions.

3. Build the Cython modules

   cd $KITTI_ROOT/lib
   make

4. Build Caffe and pycaffe

   cd $KITTI_ROOT/caffe-fast-rcnn
   # Now follow the Caffe installation instructions here:
   #   http://caffe.berkeleyvision.org/installation.html
   
   # If you're experienced with Caffe and have all of the requirements installed
   # and your Makefile.config in place, then simply do:
   make -j8 && make pycaffe

5. Download pre-computed Faster R-CNN detectors

   cd $KITTI_ROOT
   ./data/scripts/fetch_faster_rcnn_models.sh

   Please download the pre-computed kitti model here:
   http://pan.baidu.com/s/1dEZOXOl password: w8n4
   And place the .caffenet model file under KITTI_ROOT/data/kitti_models/

6. Create symlinks for the kitti dataset (Not necessary if you wish only to run test)

     cd $KITTI_ROOT/data
     ln -s $kitti kitti

   Please make sure the kitti dataset has this basic structure

     $kitti/                         # kitti dataset
     $kitti/image                    # holds all the images
     $kitti/image/train              # training images
     #kitti/label                    # text annotations

To train a kitti vehicle detector.

cd $KITTI_ROOT
mkdir output
./experiments/scripts/train_kitti.sh [GPU_ID] 
# GPU_ID is the GPU you want to train on

Output will be in KITTI_ROOT/output/

To test a kitti vehicle detector.

cd $KITTI_ROOT
mkdir results
./experiments/scripts/test_kitti.sh [GPU_ID] [TEST_DIR]
# GPU_ID is the GPU you want to train on
# TEST_DIR is the directory containing test images (default to data/kitti/image/test)

Output will be in TEST_DIR/label. If TEST_DIR is not specified, output will be stored at KITTI_ROOT/results/

We ran our detector on a test image set given by TA. If you wish to evaluate the results, please download here:
http://pan.baidu.com/s/1hsc0Fzu password: 4pny
Note: These results do not include class specification. If you want the alternative, please re-run the test procedure as described above.

We implemented matlab code to draw PR curve given test text files. Please refer to ./evaluation for codes and furthur instructions.
Note: This implementation is for results with classifications.

We wrote a matlab gui program to display detection results. Please refer to ./gui for codes and furthur instructions.
Note: This implementation is for results with classifications.