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https://github.com/lufficc/ssd
High quality, fast, modular reference implementation of SSD in PyTorch
https://github.com/lufficc/ssd
computer-vision deep-learning object-detection pytorch ssd
Last synced: 4 days ago
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High quality, fast, modular reference implementation of SSD in PyTorch
- Host: GitHub
- URL: https://github.com/lufficc/ssd
- Owner: lufficc
- License: mit
- Created: 2018-12-06T20:19:33.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2024-01-26T01:50:32.000Z (11 months ago)
- Last Synced: 2024-12-20T12:06:10.972Z (4 days ago)
- Topics: computer-vision, deep-learning, object-detection, pytorch, ssd
- Language: Python
- Homepage:
- Size: 746 KB
- Stars: 1,538
- Watchers: 24
- Forks: 384
- Open Issues: 40
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# High quality, fast, modular reference implementation of SSD in PyTorch 1.0
This repository implements [SSD (Single Shot MultiBox Detector)](https://arxiv.org/abs/1512.02325). The implementation is heavily influenced by the projects [ssd.pytorch](https://github.com/amdegroot/ssd.pytorch), [pytorch-ssd](https://github.com/qfgaohao/pytorch-ssd) and [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark). This repository aims to be the code base for researches based on SSD.
Example SSD output (vgg_ssd300_voc0712).
| Losses | Learning rate | Metrics |
| :-----------: |:-------------:| :------:|
|  |  |  |
## Highlights
- **PyTorch 1.0**: Support PyTorch 1.0 or higher.
- **Multi-GPU training and inference**: We use `DistributedDataParallel`, you can train or test with arbitrary GPU(s), the training schema will change accordingly.
- **Modular**: Add your own modules without pain. We abstract `backbone`,`Detector`, `BoxHead`, `BoxPredictor`, etc. You can replace every component with your own code without change the code base. For example, You can add [EfficientNet](https://github.com/lukemelas/EfficientNet-PyTorch) as backbone, just add `efficient_net.py` (ALREADY ADDED) and register it, specific it in the config file, It's done!
- **CPU support for inference**: runs on CPU in inference time.
- **Smooth and enjoyable training procedure**: we save the state of model, optimizer, scheduler, training iter, you can stop your training and resume training exactly from the save point without change your training `CMD`.
- **Batched inference**: can perform inference using multiple images per batch per GPU.
- **Evaluating during training**: eval you model every `eval_step` to check performance improving or not.
- **Metrics Visualization**: visualize metrics details in tensorboard, like AP, APl, APm and APs for COCO dataset or mAP and 20 categories' AP for VOC dataset.
- **Auto download**: load pre-trained weights from URL and cache it.
## Installation
### Requirements
1. Python3
1. PyTorch 1.0 or higher
1. yacs
1. [Vizer](https://github.com/lufficc/Vizer)
1. GCC >= 4.9
1. OpenCV
### Step-by-step installation
```bash
git clone https://github.com/lufficc/SSD.git
cd SSD
# Required packages: torch torchvision yacs tqdm opencv-python vizer
pip install -r requirements.txt
# Done! That's ALL! No BUILD! No bothering SETUP!
# It's recommended to install the latest release of torch and torchvision.
```
## Train
### Setting Up Datasets
#### Pascal VOC
For Pascal VOC dataset, make the folder structure like this:
```
VOC_ROOT
|__ VOC2007
|_ JPEGImages
|_ Annotations
|_ ImageSets
|_ SegmentationClass
|__ VOC2012
|_ JPEGImages
|_ Annotations
|_ ImageSets
|_ SegmentationClass
|__ ...
```
Where `VOC_ROOT` default is `datasets` folder in current project, you can create symlinks to `datasets` or `export VOC_ROOT="/path/to/voc_root"`.
#### COCO
For COCO dataset, make the folder structure like this:
```
COCO_ROOT
|__ annotations
|_ instances_valminusminival2014.json
|_ instances_minival2014.json
|_ instances_train2014.json
|_ instances_val2014.json
|_ ...
|__ train2014
|_ .jpg
|_ ...
|_ .jpg
|__ val2014
|_ .jpg
|_ ...
|_ .jpg
|__ ...
```
Where `COCO_ROOT` default is `datasets` folder in current project, you can create symlinks to `datasets` or `export COCO_ROOT="/path/to/coco_root"`.
### Single GPU training
```bash
# for example, train SSD300:
python train.py --config-file configs/vgg_ssd300_voc0712.yaml
```
### Multi-GPU training
```bash
# for example, train SSD300 with 4 GPUs:
export NGPUS=4
python -m torch.distributed.launch --nproc_per_node=$NGPUS train.py --config-file configs/vgg_ssd300_voc0712.yaml SOLVER.WARMUP_FACTOR 0.03333 SOLVER.WARMUP_ITERS 1000
```
The configuration files that I provide assume that we are running on single GPU. When changing number of GPUs, hyper-parameter (lr, max_iter, ...) will also changed according to this paper: [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://arxiv.org/abs/1706.02677).
## Evaluate
### Single GPU evaluating
```bash
# for example, evaluate SSD300:
python test.py --config-file configs/vgg_ssd300_voc0712.yaml
```
### Multi-GPU evaluating
```bash
# for example, evaluate SSD300 with 4 GPUs:
export NGPUS=4
python -m torch.distributed.launch --nproc_per_node=$NGPUS test.py --config-file configs/vgg_ssd300_voc0712.yaml
```
## Demo
Predicting image in a folder is simple:
```bash
python demo.py --config-file configs/vgg_ssd300_voc0712.yaml --images_dir demo --ckpt https://github.com/lufficc/SSD/releases/download/1.2/vgg_ssd300_voc0712.pth
```
Then it will download and cache `vgg_ssd300_voc0712.pth` automatically and predicted images with boxes, scores and label names will saved to `demo/result` folder by default.
You will see a similar output:
```text
(0001/0005) 004101.jpg: objects 01 | load 010ms | inference 033ms | FPS 31
(0002/0005) 003123.jpg: objects 05 | load 009ms | inference 019ms | FPS 53
(0003/0005) 000342.jpg: objects 02 | load 009ms | inference 019ms | FPS 51
(0004/0005) 008591.jpg: objects 02 | load 008ms | inference 020ms | FPS 50
(0005/0005) 000542.jpg: objects 01 | load 011ms | inference 019ms | FPS 53
```
## MODEL ZOO
### Origin Paper:
| | VOC2007 test | coco test-dev2015 |
| :-----: | :----------: | :----------: |
| SSD300* | 77.2 | 25.1 |
| SSD512* | 79.8 | 28.8 |
### COCO:
| Backbone | Input Size | box AP | Model Size | Download |
| :------------: | :----------:| :--------------------------: | :--------: | :-------: |
| VGG16 | 300 | 25.2 | 262MB | [model](https://github.com/lufficc/SSD/releases/download/1.2/vgg_ssd300_coco_trainval35k.pth) |
| VGG16 | 512 | 29.0 | 275MB | [model](https://github.com/lufficc/SSD/releases/download/1.2/vgg_ssd512_coco_trainval35k.pth) |
### PASCAL VOC:
| Backbone | Input Size | mAP | Model Size | Download |
| :--------------: | :----------:| :--------------------------: | :--------: | :-------: |
| VGG16 | 300 | 77.7 | 201MB | [model](https://github.com/lufficc/SSD/releases/download/1.2/vgg_ssd300_voc0712.pth) |
| VGG16 | 512 | 80.7 | 207MB | [model](https://github.com/lufficc/SSD/releases/download/1.2/vgg_ssd512_voc0712.pth) |
| Mobilenet V2 | 320 | 68.9 | 25.5MB | [model](https://github.com/lufficc/SSD/releases/download/1.2/mobilenet_v2_ssd320_voc0712_v2.pth) |
| Mobilenet V3 | 320 | 69.5 | 29.9MB | [model](https://github.com/lufficc/SSD/releases/download/1.2/mobilenet_v3_ssd320_voc0712.pth) |
| EfficientNet-B3 | 300 | 73.9 | 97.1MB | [model](https://github.com/lufficc/SSD/releases/download/1.2/efficient_net_b3_ssd300_voc0712.pth) |
## Develop Guide
If you want to add your custom components, please see [DEVELOP_GUIDE.md](DEVELOP_GUIDE.md) for more details.
## Troubleshooting
If you have issues running or compiling this code, we have compiled a list of common issues in [TROUBLESHOOTING.md](TROUBLESHOOTING.md). If your issue is not present there, please feel free to open a new issue.
## Citations
If you use this project in your research, please cite this project.
```text
@misc{lufficc2018ssd,
author = {Congcong Li},
title = {{High quality, fast, modular reference implementation of SSD in PyTorch}},
year = {2018},
howpublished = {\url{https://github.com/lufficc/SSD}}
}
```