VGG-or-MobileNet-SSD: Caffe implementation of Google VGG/MobileNet/ShuffleNet SSD detection network. Ref: (https://github.com/chuanqi305/MobileNet-SSD) (https://github.com/weiliu89/caffe) SSD QQ交流群：581437405

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# SSD: Single Shot MultiBox Detector

[![Build Status](https://travis-ci.org/weiliu89/caffe.svg?branch=ssd)](https://travis-ci.org/weiliu89/caffe)
[![License](https://img.shields.io/badge/license-BSD-blue.svg)](LICENSE)

By [Wei Liu](http://www.cs.unc.edu/~wliu/), [Dragomir Anguelov](https://www.linkedin.com/in/dragomiranguelov), [Dumitru Erhan](http://research.google.com/pubs/DumitruErhan.html), [Christian Szegedy](http://research.google.com/pubs/ChristianSzegedy.html), [Scott Reed](http://www-personal.umich.edu/~reedscot/), [Cheng-Yang Fu](http://www.cs.unc.edu/~cyfu/), [Alexander C. Berg](http://acberg.com).

### Introduction

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 our [arXiv paper](http://arxiv.org/abs/1512.02325) and our [slide](http://www.cs.unc.edu/~wliu/papers/ssd_eccv2016_slide.pdf).

| System | VOC2007 test *mAP* | **FPS** (Titan X) | Number of Boxes | Input resolution
|:-------|:-----:|:-------:|:-------:|:-------:|
| [Faster R-CNN (VGG16)](https://github.com/ShaoqingRen/faster_rcnn) | 73.2 | 7 | ~6000 | ~1000 x 600 |
| [YOLO (customized)](http://pjreddie.com/darknet/yolo/) | 63.4 | 45 | 98 | 448 x 448 |
| SSD300* (VGG16) | 77.2 | 46 | 8732 | 300 x 300 |
| SSD512* (VGG16) | **79.8** | 19 | 24564 | 512 x 512 |

_Note: SSD300* and SSD512* are the latest models. Current code should reproduce these results._

### Citing SSD

Please cite SSD in your publications if it helps your research:

@inproceedings{liu2016ssd,
      title = {{SSD}: Single Shot MultiBox Detector},
      author = {Liu, Wei and Anguelov, Dragomir and Erhan, Dumitru and Szegedy, Christian and Reed, Scott and Fu, Cheng-Yang and Berg, Alexander C.},
      booktitle = {ECCV},
      year = {2016}
    }

### Contents
1. [Installation](#installation)
2. [Preparation](#preparation)
3. [Train/Eval](#traineval)
4. [Models](#models)

### Installation
1. Get the code. We will call the directory that you cloned Caffe into `$CAFFE_ROOT`
  ```Shell
  git clone https://github.com/weiliu89/caffe.git
  cd caffe
  git checkout ssd
  ```

2. Build the code. Please follow [Caffe instruction](http://caffe.berkeleyvision.org/installation.html) to install all necessary packages and build it.
  ```Shell
  # 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](https://gist.github.com/weiliu89/2ed6e13bfd5b57cf81d6). 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/`
  ```Shell
  # 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.
  ```Shell
  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.
  ```Shell
  # 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](https://drive.google.com/open?id=0BzKzrI_SkD1_WVVTSmQxU0dVRzA).

2. Evaluate the most recent snapshot.
  ```Shell
  # 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 <kbd>esc</kbd> to stop.
 ```Shell
 # If you would like to attach a webcam to a model you trained, you can do:
 python examples/ssd/ssd_pascal_webcam.py
 ```
 [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.

4. Check out [`examples/ssd_detect.ipynb`](https://github.com/weiliu89/caffe/blob/ssd/examples/ssd_detect.ipynb) or [`examples/ssd/ssd_detect.cpp`](https://github.com/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://github.com/weiliu89/caffe/blob/ssd/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`](https://github.com/weiliu89/caffe/blob/ssd/examples/ssd_detect.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](https://arxiv.org/pdf/1512.02325v4.pdf), most models contain a pretrained `.caffemodel` file, many `.prototxt` files, and python scripts.

1. PASCAL VOC models:
 * 07+12: [SSD300*](https://drive.google.com/open?id=0BzKzrI_SkD1_WVVTSmQxU0dVRzA), [SSD512*](https://drive.google.com/open?id=0BzKzrI_SkD1_ZDIxVHBEcUNBb2s)
 * 07++12: [SSD300*](https://drive.google.com/open?id=0BzKzrI_SkD1_WnR2T1BGVWlCZHM), [SSD512*](https://drive.google.com/open?id=0BzKzrI_SkD1_MjFjNTlnempHNWs)
 * COCO[1]: [SSD300*](https://drive.google.com/open?id=0BzKzrI_SkD1_NDlVeFJDc2tIU1k), [SSD512*](https://drive.google.com/open?id=0BzKzrI_SkD1_TW4wTC14aDdCTDQ)
 * 07+12+COCO: [SSD300*](https://drive.google.com/open?id=0BzKzrI_SkD1_UFpoU01yLS1SaG8), [SSD512*](https://drive.google.com/open?id=0BzKzrI_SkD1_X3ZXQUUtM0xNeEk)
 * 07++12+COCO: [SSD300*](https://drive.google.com/open?id=0BzKzrI_SkD1_TkFPTEQ1Z091SUE), [SSD512*](https://drive.google.com/open?id=0BzKzrI_SkD1_NVVNdWdYNEh1WTA)

2. COCO models:
   * trainval35k: [SSD300*](https://drive.google.com/open?id=0BzKzrI_SkD1_dUY1Ml9GRTFpUWc), [SSD512*](https://drive.google.com/open?id=0BzKzrI_SkD1_dlJpZHJzOXd3MTg)

3. ILSVRC models:
   * trainval1: [SSD300*](https://drive.google.com/open?id=0BzKzrI_SkD1_a2NKQ2d1d043VXM), [SSD500](https://drive.google.com/open?id=0BzKzrI_SkD1_X2ZCLVgwLTgzaTQ)

[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.

# MobileNet-SSD
A caffe implementation of MobileNet-SSD detection network, with pretrained weights on VOC0712 and mAP=0.727.

### Run
1. Download [SSD](https://github.com/weiliu89/caffe/tree/ssd) source code and compile (follow the SSD README).
2. Download the pretrained deploy weights from the link above.
3. Put all the files in SSD_HOME/examples/
4. Run demo.py to show the detection result.

### Train your own dataset
1. Convert your own dataset to lmdb database (follow the SSD README), and create symlinks to current directory.
```
ln -s PATH_TO_YOUR_TRAIN_LMDB trainval_lmdb
ln -s PATH_TO_YOUR_TEST_LMDB test_lmdb
```
2. Create the labelmap.prototxt file and put it into current directory.
3. Use gen_model.sh to generate your own training prototxt.
4. Download the training weights from the link above, and run train.sh, after about 30000 iterations, the loss should be 1.5 - 2.5.
5. Run test.sh to evaluate the result.
6. Run merge_bn.py to generate your own deploy caffemodel.

### About some details
There are 2 primary differences between this model and [MobileNet-SSD on tensorflow](https://github.com/tensorflow/models/blob/master/object_detection/g3doc/detection_model_zoo.md):
1. ReLU6 layer is replaced by ReLU.
2. For the conv11_mbox_prior layer, the anchors is [(0.2, 1.0), (0.2, 2.0), (0.2, 0.5)] vs tensorflow's [(0.1, 1.0), (0.2, 2.0), (0.2, 0.5)].

### Reproduce the result
I trained this model from a MobileNet classifier([caffemodel](https://drive.google.com/open?id=0B3gersZ2cHIxZi13UWF0OXBsZzA) and [prototxt](https://drive.google.com/open?id=0B3gersZ2cHIxWGEzbG5nSXpNQzA)) converted from [tensorflow](http://download.tensorflow.org/models/mobilenet_v1_1.0_224_2017_06_14.tar.gz). 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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