Some experiments evaluated on CompCar dataset

Created by nicklhy(at gmail dot com)

Dataset reference @InProceedings{Yang_2015_CVPR, author = {Yang, Linjie and Luo, Ping and Change Loy, Chen and Tang, Xiaoou}, title = {A Large-Scale Car Dataset for Fine-Grained Categorization and Verification}, journal = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2015} }

Experimental Results

1. Make level recognition rate
2. Model level recognition rate
3. All results(table)

Introduction

Requirements

1. [Requirements: software]
2. [Requirements: hardware]

Requirements: software

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

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

```make
# In your Makefile.config, make sure to have this line uncommented
WITH_PYTHON_LAYER := 1
```

Caffe build with mkl, cudnn is strongly recommended. 2. For fast-rcnn based classification experiments, fast-rcnn is needed. 3. For xgboost based experiments, xgboost is needed. 4. Python packages you might not have: python-numpy, python-scipy, python-matplotlib, python-opencv, python-scikit-learn.

Requirements: hardware

1. For training large CNN networks (VGG16, GoogleNet), a good GPU (e.g., Titan, K20, K40, ...) is needed.
2. Other non-deep-learning methods have no specific hardware requirements.

Instructions

1. Prepare the dataset Download the CompCar dataset at any place in you hard disk and build a soft link to our repo’s root directory as the name data:

   ln -s /path/to/CompCar /path/to/CompCar_Analysis/data
   

2. Split and transform the original dataset into some specific forms

• To split the vehicle images for training and testing into different angles, use tools/split_viewpoints.py

  ./tools/split_viewpoints.py
  

• To crop all vehicles from the original images

  ./tools/generate_cropped_image.py 4
  # the argument `4` is the process num we use to accelerate the program, default is 2(multi-thread is useless in Python, thus, we choose multi-process).
  

• Generate label list files ./tools/generate_label_list.py will generate the label included list files.

  ./tools/generate_label_list.py data/train_test_split/classification/train.txt make
  # You can substitute `${phase}${_viewpoint}.txt`({phase: [train, test], _viewpoints: [``, `_front`, `_rear`, `_side`, `_front_side`, `_rear_side`]}) for `train.txt` and subsitute `${level}`({level: [make, model]}) for `make`, which will generate a new `phase_viewpoint_level.txt` list file with labels after the image name.
  

3. Train a CNN classifier:

• src/caffe/train.py: classifier training code
• src/caffe/evaluation.py: classifier evaluation code
• src/caffe/extract_deep_feature.py: CNN feature extractor