RFB Net Detector

LICENSE

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+MIT License
+
+Copyright (c) 2017 Max deGroot, Ellis Brown
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+#Receptive Field Block Net for Accurate and Fast Object Detection
+
+By Songtao Liu, Di Huang, Yunhong Wang
+
+###Introduction
+Inspired by the structure of Receptive Fields (RFs) in human visual systems, we propose a novel RF Block (RFB) module, which takes the relationship between the size and eccentricity of RFs into account, to enhance the discriminability and robustness of features. We further  assemble the RFB module to the top of SSD with a lightweight CNN model, constructing the RFB Net detector. You can use the code to train/evaluate the RFB Net for object detection. For more details, please refer to our [arXiv paper](https://arxiv.org/pdf/1711.07767.pdf). 
+
+###VOC2007 Test
+| System |  *mAP* | **FPS** (Titan X Maxwell) |
+|:-------|:-----:|:-------:|
+| [Faster R-CNN (VGG16)](https://github.com/ShaoqingRen/faster_rcnn) | 73.2 | 7 | 
+| [YOLOv2 (Darknet-19)](http://pjreddie.com/darknet/yolo/) | 78.6 | 40 | 
+| [R-FCN (ResNet-101)](https://github.com/daijifeng001/R-FCN)| 80.5| 9 |
+| [SSD300* (VGG16)](https://github.com/weiliu89/caffe/tree/ssd) | 77.2 | 46 |
+| [SSD512* (VGG16)](https://github.com/weiliu89/caffe/tree/ssd) | 79.8 | 19 |
+| RFBNet300 (VGG16) | **80.5** |**83\*** | 
+| RFBNet512 (VGG16) | **82.2** | **38\*** | 
+
+###COCO 
+| System |  *test-dev mAP* | **Time** (Titan X Maxwell) |
+|:-------|:-----:|:-------:|
+| [Faster R-CNN++ (ResNet-101)](https://github.com/KaimingHe/deep-residual-networks) | 34.9 | 3.36s | 
+| [YOLOv2 (Darknet-19)](http://pjreddie.com/darknet/yolo/) | 21.6 | 25ms| 
+| [SSD300* (VGG16)](https://github.com/weiliu89/caffe/tree/ssd) | 25.1 | 22ms |
+| [SSD512* (VGG16)](https://github.com/weiliu89/caffe/tree/ssd) | 28.8 | 53ms |
+| [RetinaNet500 (ResNet-101-FPN)](https://arxiv.org/pdf/1708.02002.pdf) | 34.4| 90ms|
+| RFBNet300 (VGG16) | **29.9** |**15ms\*** | 
+| RFBNet512 (VGG16) | **33.8** | **30ms\*** |
+| RFBNet512-E (VGG16) | **34.4** | **33ms\*** |  
+*Note*: **\*** The speed here is tested on the newest pytorch and cudnn version (0.2.0 and cudnnV6), which is obviously faster than the speed reported in paper (using pytorch-0.1.12 and cudnnV5).
+
+###MobileNet
+|System |COCO *minival mAP*| **\#parameters**|
+|:-------|:-----:|:-------:|
+|[SSD MobileNet](https://arxiv.org/abs/1704.04861)| 19.3| 6.8M|
+|RFB MobileNet| 20.7\* | 7.4M|
+\*: slightly better than original ones in paper (20.5). 
+###Citing RFB Net
+Please cite our paper in your publications if it helps your research:
+
+ @article{liu2017RFB,
+      title = {Receptive Field Block Net for Accurate and Fast Object Detection},
+      author = {Songtao Liu, Di Huang and Yunhong Wang},
+      booktitle = {arxiv preprint arXiv:1711.07767},
+      year = {2017}
+    }
+
+###Contents
+1. [Installation](#installation)
+2. [Datasets](#datasets)
+3. [Training](#training)
+4. [Evaluation](#evaluation)
+5. [Models](#models)
+
+## Installation
+- Install [PyTorch-0.2.0](http://pytorch.org/) by selecting your environment on the website and running the appropriate command.
+- Clone this repository. This repository is mainly based on [ssd.pytorch](https://github.com/amdegroot/ssd.pytorch) and [Chainer-ssd](https://github.com/Hakuyume/chainer-ssd), a huge thank to them.
+  * Note: We currently only support Python 3+.
+- Compile the nms and coco tools:
+```Shell
+./make.sh
+```
+*Note*: Check you GPU architecture support in utils/build.py, line 131. Default is:
+``` 
+'nvcc': ['-arch=sm_52',
+```
+- Install [pyinn](https://github.com/szagoruyko/pyinn) for MobileNet backbone:
+```Shell
+pip install git+https://github.com/szagoruyko/pyinn.git@master
+```
+- Then download the dataset by following the [instructions](#download-voc2007-trainval--test) below and install opencv. 
+```Shell
+conda install opencv
+```
+Note: For training, we currently  support [VOC](http://host.robots.ox.ac.uk/pascal/VOC/) and [COCO](http://mscoco.org/). 
+
+## Datasets
+To make things easy, we provide simple VOC and COCO dataset loader that inherits `torch.utils.data.Dataset` making it fully compatible with the `torchvision.datasets` [API](http://pytorch.org/docs/torchvision/datasets.html).
+
+### VOC Dataset
+##### Download VOC2007 trainval & test
+
+```Shell
+# specify a directory for dataset to be downloaded into, else default is ~/data/
+sh data/scripts/VOC2007.sh # <directory>
+```
+
+##### Download VOC2012 trainval
+
+```Shell
+# specify a directory for dataset to be downloaded into, else default is ~/data/
+sh data/scripts/VOC2012.sh # <directory>
+```
+### COCO Dataset
+Install the MS COCO dataset at /path/to/coco from [official website](http://mscoco.org/), default is ~/data/COCO. Following the [instructions](https://github.com/rbgirshick/py-faster-rcnn/blob/77b773655505599b94fd8f3f9928dbf1a9a776c7/data/README.md) to prepare *minival2014* and *valminusminival2014* annotations. All label files (.json) should be under the COCO/annotations/ folder. It should have this basic structure
+```Shell
+$COCO/
+$COCO/cache/
+$COCO/annotations/
+$COCO/images/
+$COCO/images/test2015/
+$COCO/images/train2014/
+$COCO/images/val2014/
+```
+*UPDATE*: The current COCO dataset has released new *train2017* and *val2017* sets which are just new splits of the same image sets. 
+
+## Training
+- First download the fc-reduced [VGG-16](https://arxiv.org/abs/1409.1556) PyTorch base network weights at:    https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth
+or from our [BaiduYun Driver](https://pan.baidu.com/s/1jIP86jW) 
+- MobileNet pre-trained basenet is ported from [MobileNet-Caffe](https://github.com/shicai/MobileNet-Caffe), which achieves slightly better accuracy rates than the original one reported in the [paper](https://arxiv.org/abs/1704.04861):
+https://drive.google.com/open?id=13aZSApybBDjzfGIdqN1INBlPsddxCK14
+[BaiduYun Driver](https://pan.baidu.com/s/1dFKZhdv)
+- By default, we assume you have downloaded the file in the `RFBNet/weights` dir.
+- To train RFBNet using the train script simply specify the parameters listed in `train_RFB.py` as a flag or manually change them.
+```Shell
+python train_RFB.py -d VOC -v RFB_vgg -s 300 
+```
+- Note:
+  * -d: choose datasets, VOC or COCO.
+  * -v: choose backbone version, RFB_VGG, RFB_E_VGG or RFB_mobile.
+  * -s: image size, 300 or 512.
+  * You can pick-up training from a checkpoint by specifying the path as one of the training parameters (again, see `train_RFB.py` for options)
+
+## Evaluation
+To evaluate a trained network:
+
+```Shell
+python test_RFB.py -d VOC -v RFB_vgg -s 300 --trained_models /path/to/model/weights
+```
+By default, it will directly output the mAP results on VOC2007 *test* or COCO *minival2014*. For VOC2012 *test* and COCO *test-dev* results, you can manually change the datasets in the `test_RFB.py` file, then save the detection results and submitted to the server. 
+
+##Models
+
+* 07+12 [RFB_Net300](https://drive.google.com/open?id=1V3DjLw1ob89G8XOuUn7Jmg_o-8k_WM3L), [BaiduYun Driver](https://pan.baidu.com/s/1bplRosf)
+* COCO [RFB_Net512_E](https://drive.google.com/open?id=1pHDc6Xg9im3affOr7xaimXaRNOHtbaPM), [BaiduYun Driver](https://pan.baidu.com/s/1o8dxrom)
+* COCO [RFB_Mobile Net300](https://drive.google.com/open?id=1vmbTWWgeMN_qKVWOeDfl1EN9c7yHPmOe), [BaiduYun Driver](https://pan.baidu.com/s/1bp4ik1L)
+
+

data/__init__.py

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+# from .voc import VOCDetection, AnnotationTransform, detection_collate, VOC_CLASSES
+from .voc0712 import VOCDetection, AnnotationTransform, detection_collate, VOC_CLASSES
+from .coco import COCODetection
+from .data_augment import *
+from .config import *