Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/lzx1413/PytorchSSD
pytorch version of SSD and it's enhanced methods such as RFBSSD,FSSD and RefineDet
https://github.com/lzx1413/PytorchSSD
fssd pytorch refinedet rfb ssd
Last synced: 3 months ago
JSON representation
pytorch version of SSD and it's enhanced methods such as RFBSSD,FSSD and RefineDet
- Host: GitHub
- URL: https://github.com/lzx1413/PytorchSSD
- Owner: lzx1413
- License: mit
- Created: 2017-12-14T13:17:03.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2020-03-27T10:35:05.000Z (over 4 years ago)
- Last Synced: 2024-07-28T07:31:51.950Z (4 months ago)
- Topics: fssd, pytorch, refinedet, rfb, ssd
- Language: Python
- Homepage:
- Size: 707 KB
- Stars: 708
- Watchers: 22
- Forks: 238
- Open Issues: 44
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-AutoML-and-Lightweight-Models - lzx1413/PytorchSSD
README
# Pytorch SSD Series
## Pytorch 4.1 is suppoted on branch 0.4 now.
## Support Arc:
* SSD [SSD: Single Shot Multibox Detector](https://arxiv.org/abs/1512.02325)
* FSSD [FSSD: Feature Fusion Single Shot Multibox Detector](https://arxiv.org/abs/1712.00960)
* RFB-SSD[Receptive Field Block Net for Accurate and Fast Object Detection](https://arxiv.org/abs/1711.07767)
* RefineDet[Single-Shot Refinement Neural Network for Object Detection](https://arxiv.org/pdf/1711.06897.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 |
| SSD300 (VGG) | 77.8 | **150 (1080Ti)** |
| FSSD300 (VGG) | 78.8 | 120 (1080Ti) |
### 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\*** |
| [SSD512 (HarDNet68)](https://github.com/PingoLH/PytorchSSD-HarDNet) | 31.7 | TBD (12.9ms\*\*) |
| [SSD512 (HarDNet85)](https://github.com/PingoLH/PytorchSSD-HarDNet) | 35.1 | TBD (15.9ms\*\*) |
| RFBNet512 (HarDNet68) | 33.9 | TBD (16.7ms\*\*) |
| RFBNet512 (HarDNet85) | 36.8 | TBD (19.3ms\*\*) |
*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 the paper (using pytorch-0.1.12 and cudnnV5).
*Note*: **\*\*** HarDNet results are measured on Titan V with pytorch 1.0.1
for detection only (NMS is NOT included, which is 13~18ms in general cases).
For reference, the measurement of SSD-vgg on the same environment is 15.7ms
(also detection only).
### 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 the original ones in the paper (20.5).
### Contents
1. [Installation](#installation)
2. [Datasets](#datasets)
3. [Training](#training)
4. [Evaluation](#evaluation)
5. [Models](#models)
## Installation
- Install [PyTorch-0.2.0-0.3.1](http://pytorch.org/) by selecting your environment on the website and running the appropriate command.
- Clone this repository. This repository is mainly based on[RFBNet](https://github.com/ruinmessi/RFBNet), [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 #
```
##### Download VOC2012 trainval
```Shell
# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/VOC2012.sh #
```
### 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), weight file is available at: https://drive.google.com/open?id=13aZSApybBDjzfGIdqN1INBlPsddxCK14 or [BaiduYun Driver](https://pan.baidu.com/s/1dFKZhdv).
- By default, we assume you have downloaded the file in the `RFBNet/weights` dir:
```Shell
mkdir weights
cd weights
wget https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth
```
- 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_test.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
The test frequency can be found in the train_test.py
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
* ImageNet [mobilenet](https://drive.google.com/open?id=11VqerLerDkFzN_fkwXG4Vm1CIU2G5Gtm)
* 07+12 [RFB_Net300](https://drive.google.com/open?id=1V3DjLw1ob89G8XOuUn7Jmg_o-8k_WM3L), [BaiduYun Driver](https://pan.baidu.com/s/1bplRosf),[FSSD300](https://drive.google.com/open?id=1xhgdxCF_HuC3SP6ALhhTeC5RTmuoLzgC),[SSD300](https://drive.google.com/open?id=10sM_yWSN8vRZdh6Sf0CILyMfcoJiCNtn)
* 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)
## Update (Sep 29, 2019)
* Add SSD and RFBNet with [Harmonic DenseNet (HarDNet)](https://github.com/PingoLH/Pytorch-HarDNet) as backbone models.
* Pretrained backbone models:
[hardnet68_base_bridge.pth](https://ping-chao.com/hardnet/hardnet68_base_bridge.pth) |
[hardnet85_base.pth](https://ping-chao.com/hardnet/hardnet85_base.pth)
* Pretrained models for COCO dataset:
[SSD512-HarDNet68](https://ping-chao.com/hardnet/SSD512_HarDNet68_COCO.pth) |
[SSD512-HarDNet85](https://ping-chao.com/hardnet/SSD512_HarDNet85_COCO.pth) |
[RFBNet512-HarDNet68](https://ping-chao.com/hardnet/RFB512_HarDNet68_COCO.pth) |
[RFBNet512-HarDNet85](https://ping-chao.com/hardnet/RFB512_HarDNet85_COCO.pth)