** AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results in the table denote val2017 accuracy.
** All AP numbers are for single-model single-scale without ensemble or test-time augmentation. Reproduce by `python test.py --img-size 736 --conf_thres 0.001`
** Latency<sub>GPU</sub> measures end-to-end latency per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 16, and includes image preprocessing, FP32 inference, postprocessing and NMS. Average NMS time included in this chart is 1.6ms. Reproduce by `python test.py --img-size 640 --conf_thres 0.1`
** All AP numbers are for single-model single-scale without ensemble or test-time augmentation. Reproduce by `python test.py --img 736 --conf 0.001`
** Latency<sub>GPU</sub> measures end-to-end latency per image averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) instance with one V100 GPU, and includes image preprocessing, pytorch FP32 inference at batch size 16, postprocessing and NMS. Average NMS time included in this chart is 1-2ms/img. Reproduce by `python test.py --img 640 --conf 0.1`
** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).