merge caffe-ssd-optimized to speedup train preprocess

SSD is an unified framework for object detection with a single network. You can use the code to train/evaluate a network for object detection task. For more details, please refer to arXiv paper and slide. Original ssd can only be built on ubuntu and used an outdated version caffe.

git clone https://github.com/imistyrain/ssd
cd ssd
scripts/build_win.cmd

git clone https://github.com/imistyrain/ssd
cd ssd
mkdir build
cd build
make -j4
make install

Note: on Mac, you should pass the PYTHON_LIBRARY by runnning cmake

cmake -DPYTHON_LIBRARY=$(python-config --prefix)/lib/libpython2.7.dylib -DPYTHON_INCLUDE_DIR=$(python-config --prefix)/include/python2.7 ..

for python3.6

cmake -DCMAKE_INSTALL_PREFIX=/usr/local/ -DPYTHON_LIBRARY=$(python3-config --prefix)/lib/libpython3.6.dylib -DPYTHON_INCLUDE_DIR=$(python3-config --prefix)/include/python3.6m ..

otherwise you would get error:

 8102 segmentation fault

what' more, you should import caffe at the beginning of script before import cv2 to avoid the error of

src/tcmalloc.cc:284] Attempt to free invalid pointer 0x7ff4821267d0

2021.01.16 add UpsampleLayer from caffe-segnet-cudnn5 2020.11.10 add focalloss layer and shuffle layer 2020.11.14 fix bug863: training error: Data layer prefetch queue empty 2020.12.13 add InterpLayer

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

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.

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

you can reference ssd-models if you want to train your model on your own data such like face detection.