Improved SSD for small object detection: Context Single Shot MultiBox Detector

Original Network

Context SSD

Contents

1. Installation
2. Preparation
3. Train/Eval
4. Models

Installation

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
make -j8
# Make sure to include $CAFFE_ROOT/python to your PYTHONPATH.
make py
make test -j8
# (Optional)
make runtest -j8

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.

cd $CAFFE_ROOT
# Create the trainval.txt, test.txt, and test_name_size.txt in data/VOC0712/
./data/VOC0712/create_list.sh
# You can modify the parameters in create_data.sh if needed.
# It will create lmdb files for trainval and test with encoded original image:
#   - $HOME/data/VOCdevkit/VOC0712/lmdb/VOC0712_trainval_lmdb
#   - $HOME/data/VOCdevkit/VOC0712/lmdb/VOC0712_test_lmdb
# and make soft links at examples/VOC0712/
./data/VOC0712/create_data.sh

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.

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.