开源软件名称(OpenSource Name):FreeApe/VGG-or-MobileNet-SSD开源软件地址(OpenSource Url):https://github.com/FreeApe/VGG-or-MobileNet-SSD开源编程语言(OpenSource Language):C++ 80.3%开源软件介绍(OpenSource Introduction):SSD QQ交流群(QQ Communication group)
SSD: Single Shot MultiBox DetectorBy Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg. IntroductionSSD 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 our arXiv paper and our slide.
Note: SSD300* and SSD512* are the latest models. Current code should reproduce these results. Citing SSDPlease cite SSD in your publications if it helps your research:
ContentsInstallation
git clone https://github.com/weiliu89/caffe.git
cd caffe
git checkout ssd
# 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
# 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
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
# 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.
# If you would like to test a model you trained, you can do:
python examples/ssd/score_ssd_pascal.py
# 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.
ModelsWe have provided the latest models that are trained from different datasets. To help reproduce the results in Table 6, most models contain a pretrained
[1]We use MobileNet-SSDA caffe implementation of MobileNet-SSD detection network, with pretrained weights on VOC0712 and mAP=0.727.
Run
Train your own dataset
About some detailsThere are 2 primary differences between this model and MobileNet-SSD on tensorflow:
Reproduce the resultI trained this model from a MobileNet classifier(caffemodel and prototxt) converted from tensorflow. I first trained the model on MS-COCO and then fine-tuned on VOC0712. Without MS-COCO pretraining, it can only get mAP=0.68. |
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