| # CornerNet | |
| > [Cornernet: Detecting objects as paired keypoints](https://arxiv.org/abs/1808.01244) | |
| <!-- [ALGORITHM] --> | |
| ## Abstract | |
| We propose 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. | |
| <div align=center> | |
| <img src="https://user-images.githubusercontent.com/40661020/143876061-4de20768-c812-4b97-b089-944d8db91ca2.png"/> | |
| </div> | |
| ## Results and Models | |
| | Backbone | Batch Size | Step/Total Epochs | Mem (GB) | Inf time (fps) | box AP | Config | Download | | |
| | :--------------: | :---------------------------------------------------------: | :---------------: | :------: | :------------: | :----: | :-------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | |
| | HourglassNet-104 | [10 x 5](./cornernet_hourglass104_mstest_10x5_210e_coco.py) | 180/210 | 13.9 | 4.2 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco/cornernet_hourglass104_mstest_10x5_210e_coco_20200824_185720-5fefbf1c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco/cornernet_hourglass104_mstest_10x5_210e_coco_20200824_185720.log.json) | | |
| | HourglassNet-104 | [8 x 6](./cornernet_hourglass104_mstest_8x6_210e_coco.py) | 180/210 | 15.9 | 4.2 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco/cornernet_hourglass104_mstest_8x6_210e_coco_20200825_150618-79b44c30.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco/cornernet_hourglass104_mstest_8x6_210e_coco_20200825_150618.log.json) | | |
| | HourglassNet-104 | [32 x 3](./cornernet_hourglass104_mstest_32x3_210e_coco.py) | 180/210 | 9.5 | 3.9 | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco/cornernet_hourglass104_mstest_32x3_210e_coco_20200819_203110-1efaea91.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco/cornernet_hourglass104_mstest_32x3_210e_coco_20200819_203110.log.json) | | |
| Note: | |
| - TTA setting is single-scale and `flip=True`. | |
| - Experiments with `images_per_gpu=6` are conducted on Tesla V100-SXM2-32GB, `images_per_gpu=3` are conducted on GeForce GTX 1080 Ti. | |
| - Here are the descriptions of each experiment setting: | |
| - 10 x 5: 10 GPUs with 5 images per gpu. This is the same setting as that reported in the original paper. | |
| - 8 x 6: 8 GPUs with 6 images per gpu. The total batchsize is similar to paper and only need 1 node to train. | |
| - 32 x 3: 32 GPUs with 3 images per gpu. The default setting for 1080TI and need 4 nodes to train. | |
| ## Citation | |
| ```latex | |
| @inproceedings{law2018cornernet, | |
| title={Cornernet: Detecting objects as paired keypoints}, | |
| author={Law, Hei and Deng, Jia}, | |
| booktitle={15th European Conference on Computer Vision, ECCV 2018}, | |
| pages={765--781}, | |
| year={2018}, | |
| organization={Springer Verlag} | |
| } | |
| ``` | |