NotePublic/Software/Applications/Caffe/Caffe_SSD_目标检测.md

Caffe SSD 目标检测

基于 Ubuntu16.04 和 Python2.7。

1.Installation

按《Ubuntu 初始配置》中的方法来安装开发工具。

1. Get the code. We will call the directory that you cloned Caffe into $CAFFE_ROOT

   git clone https://github.com/weiliu89/caffe.git
   cd caffe
   git checkout ssd
   

2. Build the code. Please follow Caffe instruction to install all necessary packages and build it.

   # Modify Makefile.config according to your Caffe installation.
   cp Makefile.config.example Makefile.config
   # 通过去除 Makefile.config 中“CPU_ONLY := 1”前面的 “#” 号可选择编译 CPU 版本的 Caffe
   make -j8
   # Make sure to include $CAFFE_ROOT/python to your PYTHONPATH.
   make py
   make test -j8
   # (Optional)
   make runtest -j8
   cd python
   for req in $(cat requirements.txt); do pip install $req; done
   pip install -r requirements.txt
   export PYTHONPATH=$PYTHONPATH:$CAFFE_ROOT/python
   cd ..
   make pycaffe
   # 验证 pycaffe 接口
   python
   >>>import caffe
   >>>exit()
   

2.Preparation

1. Download fully convolutional reduced (atrous) VGGNet. By default, we assume the model is stored in $CAFFE_ROOT/models/VGGNet/

2. Download VOC2007 and VOC2012 dataset. By default, we assume the data is stored in $HOME/data/

   # Download the data.
   cd $HOME/data
   wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
   wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
   wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
   # Extract the data.
   tar -xvf VOCtrainval_11-May-2012.tar
   tar -xvf VOCtrainval_06-Nov-2007.tar
   tar -xvf VOCtest_06-Nov-2007.tar
   

3. Create the LMDB file.

3.Train/Eval

1. Train your model and evaluate the model on the fly.

   # It will create model definition files and save snapshot models in:
   #   - $CAFFE_ROOT/models/VGGNet/VOC0712/SSD_300x300/
   # and job file, log file, and the python script in:
   #   - $CAFFE_ROOT/jobs/VGGNet/VOC0712/SSD_300x300/
   # and save temporary evaluation results in:
   #   - $HOME/data/VOCdevkit/results/VOC2007/SSD_300x300/
   # It should reach 77.* mAP at 120k iterations.
   python examples/ssd/ssd_pascal.py
   

   If you don't have time to train your model, you can download a pre-trained model at here.

2. Evaluate the most recent snapshot.

   # If you would like to test a model you trained, you can do:
   python examples/ssd/score_ssd_pascal.py
   

3. Test your model using a webcam. Note: press "esc" to stop.

   # If you would like to attach a webcam to a model you trained, you can do:
   python examples/ssd/ssd_pascal_webcam.py
   

   Here is a demo video of running a SSD500 model trained on MSCOCO dataset.

4. Check out examples/ssd_detect.ipynb or examples/ssd/ssd_detect.cpp on how to detect objects using a SSD model. Check out examples/ssd/plot_detections.py on how to plot detection results output by ssd_detect.cpp.

5. To train on other dataset, please refer to data/OTHERDATASET for more details. We currently add support for COCO and ILSVRC2016. We recommend using examples/ssd.ipynb to check whether the new dataset is prepared correctly.

4.Models

We have provided the latest models that are trained from different datasets. To help reproduce the results in Table 6, most models contain a pretrained .caffemodel file, many .prototxt files, and python scripts.

1. PASCAL VOC models:

   • 07+12: SSD300*, SSD512*
   • 07++12: SSD300*, SSD512*
   • COCO[1]: SSD300*, SSD512*
   • 07+12+COCO: SSD300*, SSD512*
   • 07++12+COCO: SSD300*, SSD512*

2. COCO models:

   • trainval35k: SSD300*, SSD512*

3. ILSVRC models:

   • trainval1: SSD300*, SSD500

[1]We use examples/convert_model.ipynb to extract a VOC model from a pretrained COCO model.