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
pip install torch torchvision yacs tqdm opencv-python vizer
# Optional packages
# If you want visualize loss curve. Default is enabled. Disable by using --use_tensorboard 0 when training.
pip install tensorboardX
# If you train coco dataset, must install cocoapi.
cd ~/github
git clone https://github.com/cocodataset/cocoapi.git
cd cocoapi/PythonAPI
python setup.py build_ext install
```
### Build
If your torchvision >= 0.3.0, nms build is not needed! We also provide a python-like nms, but is very slower than build-version.
```bash
# For faster inference you need to build nms, this is needed when evaluating. Only training doesn't need this.
cd ext
python build.py build_ext develop
```
## 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
|_