SSD

Model description

We present a method for detecting objects in images using a single deep neural network. Our approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape. Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes. Our SSD model is simple relative to methods that require object proposals because it completely eliminates proposal generation and subsequent pixel or feature resampling stage and encapsulates all computation in a single network. This makes SSD easy to train and straightforward to integrate into systems that require a detection component. Experimental results on the PASCAL VOC, MS COCO, and ILSVRC datasets confirm that SSD has comparable accuracy to methods that utilize an additional object proposal step and is much faster, while providing a unified framework for both training and inference. Compared to other single stage methods, SSD has much better accuracy, even with a smaller input image size. For 300x300 input, SSD achieves 72.1% mAP on VOC2007 test at 58 FPS on a Nvidia Titan X and for 500x500 input, SSD achieves 75.1% mAP, outperforming a comparable state of the art Faster R-CNN model. Code is available at https://github.com/weiliu89/caffe/tree/ssd .

Prepare

Download dataset

Go to visit COCO official website, then select the COCO dataset you want to download.

Take coco2017 dataset as an example, specify /path/to/coco2017 to your COCO path in later training process, the unzipped dataset path structure sholud look like:

coco2017
├── annotations
│   ├── instances_train2017.json
│   ├── instances_val2017.json
│   └── ...
├── train2017
│   ├── 000000000009.jpg
│   ├── 000000000025.jpg
│   └── ...
├── val2017
│   ├── 000000000139.jpg
│   ├── 000000000285.jpg
│   └── ...
├── train2017.txt
├── val2017.txt
└── ...

mkdir -p /home/data/perf/ssd
cd /home/data/perf/ssd
ln -s /path/to/coco/ /home/data/perf/ssd

Download backbone

cd /home/data/perf/ssd
wget https://download.pytorch.org/models/resnet34-333f7ec4.pth

Training

Multiple GPUs on one machine

## 'deepsparkhub_root_path' is the root path of deepsparkhub.
cd {deepsparkhub_root_path}/cv/detection/ssd/pytorch/base
source ../iluvatar/config/environment_variables.sh
python3  prepare.py --name iluvatar --data_dir /home/data/perf/ssd
bash run_training.sh --name iluvatar --config V100x1x8 --data_dir /home/data/perf/ssd --backbone_path /home/data/perf/ssd/resnet34-333f7ec4.pth

Results on BI-V100

Reference

https://github.com/mlcommons/training_results_v0.7/tree/master/NVIDIA/benchmarks/ssd/implementations/pytorch