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High quality, fast, modular reference implementation of SSD in PyTorch
| Date | Stars |
|---|---|
| 2026-07-24 | 1586 |
| 2026-07-25 | 1586 |
| 2026-07-28 | 1585 |
| 2026-07-30 | 1585 |
| 2026-08-18 | 1584 |
| 2026-08-19 | 1585 |
| 2026-09-02 | 1584 |
| 2026-09-14 | 1585 |
| 2026-09-15 | 1586 |
| 2026-09-16 | 1586 |
| 2026-09-20 | 1586 |
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# 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.
<div align="center">
<img src="figures/004545.jpg" width="500px" />
<p>Example SSD output (vgg_ssd300_voc0712).</p>
</div>
| 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
|_ <im-1-name>.jpg
|_ ...
|_ <im-N-name>.jpg
|__ val2014
|_ <im-1-name>.jpg
|_ ...
|_ <im-N-name>.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 Excerpt of 8,030 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e393bce7a0d8ee3f, topic:deep-learning, topic:pytorch
matched fp:e393bce7a0d8ee3f, topic:computer-vision, topic:object-detection