PADet

Main Results

	Backbone	Training	Testing	Input	FPS	mAP	[email protected]	[email protected]	mAP@S	mAP@M	mAP@L
Ours_384	VGG-16	trainval35k	test-dev	384×384	62.5	35.2	55.9	38.1	17.7	38.2	48.3
Ours_512	VGG-16	trainval35k	test-dev	512×512	38.5	37.6	58.7	41.0	21.0	40.4	49.5
Ours_384	ResNet-101	trainval35k	test-dev	384×384	40.0	36.9	57.0	40.2	16.4	40.4	53.3
Ours_512	ResNet-101	trainval35k	test-dev	512×512	28.6	40.0	60.8	43.8	21.3	43.9	53.9

Notes

• Results of our models on minival set are 34.8, 37.6, 36.7, 39.6 respectively.
• The above results are obtained by single-scale training and testing without adopting scale-jitter training and inference augmentation (e.g., multi-scale, flip, voting)
• The runtime are measured on our local machine with single NVIDIA GTX 1080 Ti, i7-6850k CPU, pyTorch 0.4.1, CUDA 9.0 and cuDNN v7.0. Different configures may induce various runtime.
• We have updated the runtime of Ours_384 with ResNet-101 since the data of last commit 42ab085 was obtained when the gpu was busy.

Comparison to other state-of-arts

Speed/Accuracy trade-off

Notes

• V means the backbone of VGG-16 and R is ResNet-101.

Todo

• Release the evaluation codes and models
• Release the training codes