CornerNet, a new approach to object detection where we detect an object bounding box as a pair of keypoints, the top-left corner and the bottom-right corner, using a single convolution neural network. By detecting objects as paired keypoints, we eliminate the need for designing a set of anchor boxes commonly used in prior single-stage detectors. In addition to our novel formulation, we introduce corner pooling, a new type of pooling layer that helps the network better localize corners. Experiments show that CornerNet achieves a 42.2% AP on MS COCO, outperforming all existing one-stage detectors.
CornerNet model is using MMDetection toolbox. Before you run this model, you need to setup MMDetection first.
# Go to "toolbox/MMDetection" directory in root path
cd ../../../../toolbox/MMDetection/
bash install_toolbox_mmdetection.sh
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
└── ...
# Make soft link to dataset
cd mmdetection/
mkdir -p data/
ln -s /path/to/coco2017 data/coco
# On single GPU
python3 tools/train.py configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py
# Multiple GPUs on one machine
bash tools/dist_train.sh configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py 8
GPUs | FP32 |
---|---|
BI-V100 x8 | MAP=41.2 |
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