162 lines
7.1 KiB
Markdown
162 lines
7.1 KiB
Markdown
# Caffe SSD 目标检测
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基于 Ubuntu16.04 和 Python2.7。
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## 1.Installation
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按《Ubuntu 初始配置》中的方法来安装开发工具。
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1. Get the code. We will call the directory that you cloned Caffe into $CAFFE_ROOT
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```bash
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git clone https://github.com/weiliu89/caffe.git
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cd caffe
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git checkout ssd
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```
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2. 修改 Makefile 文件：
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```Makefile
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-LIBRARIES += glog gflags protobuf boost_system boost_filesystem boost_regex m hdf5_hl hdf5
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+LIBRARIES += glog gflags protobuf boost_system boost_filesystem boost_regex m hdf5_serial_hl hdf5_serial
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```
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3. Build the code. Please follow Caffe instruction to install all necessary packages and build it.
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```bash
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# Modify Makefile.config according to your Caffe installation.
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cp Makefile.config.example Makefile.config
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# 通过去除 Makefile.config 中“CPU_ONLY := 1”前面的 “#” 号可选择编译 CPU 版本的 Caffe
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make -j8
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# Make sure to include $CAFFE_ROOT/python to your PYTHONPATH.
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make py
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make test -j8
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# (Optional)
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make runtest -j8
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cd python
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for req in $(cat requirements.txt); do pip install $req; done
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pip install -r requirements.txt
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export PYTHONPATH=$PYTHONPATH:$CAFFE_ROOT/python
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cd ..
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make pycaffe
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# 验证 pycaffe 接口
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python
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>>>import caffe
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>>>exit()
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```
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## 2.Preparation
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1. Download [fully convolutional reduced (atrous) VGGNet](https://gist.github.com/weiliu89/2ed6e13bfd5b57cf81d6). By default, we assume the model is stored in $CAFFE_ROOT/models/VGGNet/
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2. Download [VOC2007 and VOC2012 dataset](https://pjreddie.com/projects/pascal-voc-dataset-mirror/). By default, we assume the data is stored in $HOME/data/
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```bash
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# Download the data.
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cd $HOME/data
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wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
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wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
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wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
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# Extract the data.
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tar -xvf VOCtrainval_11-May-2012.tar
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tar -xvf VOCtrainval_06-Nov-2007.tar
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tar -xvf VOCtest_06-Nov-2007.tar
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```
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3. 修改 data/VOC0712/create_list.sh 文件：
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```bash
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-root_dir=$HOME/data/VOCdevkit/
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+root_dir=/mnt/workspace/caffe/VOC/VOCdevkit/
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```
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4. 修改 data/VOC0712/create_data.sh 文件：
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```bash
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-data_root_dir="$HOME/data/VOCdevkit"
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+data_root_dir="/mnt/workspace/caffe/VOC/VOCdevkit"
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```
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5. Create the LMDB file.
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```bash
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cd $CAFFE_ROOT
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# Create the trainval.txt, test.txt, and test_name_size.txt in data/VOC0712/
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./data/VOC0712/create_list.sh
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# You can modify the parameters in create_data.sh if needed.
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# It will create lmdb files for trainval and test with encoded original image:
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# - $HOME/data/VOCdevkit/VOC0712/lmdb/VOC0712_trainval_lmdb
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# - $HOME/data/VOCdevkit/VOC0712/lmdb/VOC0712_test_lmdb
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# and make soft links at examples/VOC0712/
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./data/VOC0712/create_data.sh
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```
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## 3.Train/Eval
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1. 修改 examples/ssd/ssd_pascal.py 文件：
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```python
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-if num_gpus > 0:
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- batch_size_per_device = int(math.ceil(float(batch_size) / num_gpus))
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- iter_size = int(math.ceil(float(accum_batch_size) / (batch_size_per_device * num_gpus)))
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- solver_mode = P.Solver.GPU
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- device_id = int(gpulist[0])
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+#if num_gpus > 0:
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+# batch_size_per_device = int(math.ceil(float(batch_size) / num_gpus))
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+# iter_size = int(math.ceil(float(accum_batch_size) / (batch_size_per_device * num_gpus)))
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+# solver_mode = P.Solver.GPU
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+# device_id = int(gpulist[0])
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```
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2. Train your model and evaluate the model on the fly.
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```bash
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# It will create model definition files and save snapshot models in:
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# - $CAFFE_ROOT/models/VGGNet/VOC0712/SSD_300x300/
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# and job file, log file, and the python script in:
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# - $CAFFE_ROOT/jobs/VGGNet/VOC0712/SSD_300x300/
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# and save temporary evaluation results in:
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# - $HOME/data/VOCdevkit/results/VOC2007/SSD_300x300/
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# It should reach 77.* mAP at 120k iterations.
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python examples/ssd/ssd_pascal.py
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```
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If you don't have time to train your model, you can download a pre-trained model at [here](https://drive.google.com/open?id=0BzKzrI_SkD1_WVVTSmQxU0dVRzA).
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3. Evaluate the most recent snapshot.
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```bash
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# If you would like to test a model you trained, you can do:
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python examples/ssd/score_ssd_pascal.py
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```
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4. Test your model using a webcam. Note: press "esc" to stop.
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```bash
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# If you would like to attach a webcam to a model you trained, you can do:
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python examples/ssd/ssd_pascal_webcam.py
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```
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[Here](https://drive.google.com/file/d/0BzKzrI_SkD1_R09NcjM1eElLcWc/view) is a demo video of running a SSD500 model trained on [MSCOCO](http://mscoco.org/) dataset.
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5. Check out [examples/ssd_detect.ipynb](https://hub.fastgit.org/weiliu89/caffe/blob/ssd/examples/ssd_detect.ipynb) or [examples/ssd/ssd_detect.cpp](https://hub.fastgit.org/weiliu89/caffe/blob/ssd/examples/ssd/ssd_detect.cpp) on how to detect objects using a SSD model. Check out [examples/ssd/plot_detections.py](https://hub.fastgit.org/weiliu89/caffe/blob/ssd/examples/ssd/plot_detections.py) on how to plot detection results output by ssd_detect.cpp.
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6. 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](https://hub.fastgit.org/weiliu89/caffe/blob/ssd/examples/ssd_detect.ipynb) to check whether the new dataset is prepared correctly.
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## 4.Models
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We have provided the latest models that are trained from different datasets. To help reproduce the results in [Table 6](https://arxiv.org/pdf/1512.02325v4.pdf), most models contain a pretrained `.caffemodel` file, many `.prototxt` files, and python scripts.
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1. PASCAL VOC models:
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* 07+12: [SSD300*](https://drive.google.com/open?id=0BzKzrI_SkD1_WVVTSmQxU0dVRzA), [SSD512*](https://drive.google.com/open?id=0BzKzrI_SkD1_ZDIxVHBEcUNBb2s)
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* 07++12: [SSD300*](https://drive.google.com/open?id=0BzKzrI_SkD1_WnR2T1BGVWlCZHM), [SSD512*](https://drive.google.com/open?id=0BzKzrI_SkD1_MjFjNTlnempHNWs)
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* COCO[1]: [SSD300*](https://drive.google.com/open?id=0BzKzrI_SkD1_NDlVeFJDc2tIU1k), [SSD512*](https://drive.google.com/open?id=0BzKzrI_SkD1_TW4wTC14aDdCTDQ)
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* 07+12+COCO: [SSD300*](https://drive.google.com/open?id=0BzKzrI_SkD1_UFpoU01yLS1SaG8), [SSD512*](https://drive.google.com/open?id=0BzKzrI_SkD1_X3ZXQUUtM0xNeEk)
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* 07++12+COCO: [SSD300*](https://drive.google.com/open?id=0BzKzrI_SkD1_TkFPTEQ1Z091SUE), [SSD512*](https://drive.google.com/open?id=0BzKzrI_SkD1_NVVNdWdYNEh1WTA)
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2. COCO models:
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* trainval35k: [SSD300*](https://drive.google.com/open?id=0BzKzrI_SkD1_dUY1Ml9GRTFpUWc), [SSD512*](https://drive.google.com/open?id=0BzKzrI_SkD1_dlJpZHJzOXd3MTg)
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3. ILSVRC models:
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* trainval1: [SSD300*](https://drive.google.com/open?id=0BzKzrI_SkD1_a2NKQ2d1d043VXM), [SSD500](https://drive.google.com/open?id=0BzKzrI_SkD1_X2ZCLVgwLTgzaTQ)
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[1]We use [`examples/convert_model.ipynb`](https://github.com/weiliu89/caffe/blob/ssd/examples/convert_model.ipynb) to extract a VOC model from a pretrained COCO model.
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