PP-YOLOE-R: An Efficient Anchor-Free Rotated Object Detector

Arbitrary-oriented object detection is a fundamental task in visual scenes involving aerial images and scene text. In this report, we present PP-YOLOE-R, an efficient anchor-free rotated object detector based on PP-YOLOE. We introduce a bag of useful tricks in PP-YOLOE-R to improve detection precision with marginal extra parameters and computational cost. As a result, PP-YOLOE-R-l and PP-YOLOE-R-x achieve 78.14 and 78.28 mAP respectively on DOTA 1.0 dataset with single-scale training and testing, which outperform almost all other rotated object detectors. With multi-scale training and testing, PP-YOLOE-R-l and PP-YOLOE-R-x further improve the detection precision to 80.02 and 80.73 mAP. In this case, PP-YOLOE-R-x surpasses all anchor-free methods and demonstrates competitive performance to state-of-the-art anchor-based two-stage models. Further, PP-YOLOE-R is deployment friendly and PP-YOLOE-R-s/m/l/x can reach 69.8/55.1/48.3/37.1 FPS respectively on RTX 2080 Ti with TensorRT and FP16-precision. Source code and pre-trained models are available at https://github.com/PaddlePaddle/PaddleDetection, which is powered by https://github.com/PaddlePaddle/Paddle.Comment: 6 pages, 2 figures, 3 table

Determinants of Disease Phenotype Differences Caused by Closely-Related Isolates of Begomovirus Betasatellites Inoculated with the Same Species of Helper Virus

Tomato yellow leaf curl China virus (TYLCCNV) is a monopartite begomovirus associated with different betasatellites. In this study, we investigate two different isolates of Tomato yellow leaf curl China betasatellite (TYLCCNB) to determine what features of the viral genome are required for induction of characteristic phenotypic differences between closely-related betasatellite. When co-agroinoculated with TYLCCNV into Nicotiana spp. and tomato plants, TYLCCNB-Y25 induced only leaf curling on all hosts, while TYLCCNB-Y10 also induced enations, vein yellowing, and shoot distortions. Further assays showed that βC1 of TYLCCNB-Y25 differs from that of TYLCCNB-Y10 in symptom induction and transcriptional modulating. Hybrid satellites were constructed in which the βC1 gene or 200 nt partial promoter-like fragment upstream of the βC1 were exchanged. Infectivity assays showed that a TYLCCNB-Y25 hybrid with the intact TYLCCNB-Y10 βC1 gene was able to induce vein yellowing, shoot distortions, and a reduced size and number of enations. A TYLCCNB-Y10 hybrid with the intact TYLCCNB-Y25 βC1 gene produced only leaf curling. In contrast, the TYLCCNB-Y25 and TYLCCNB-Y10 hybrids with swapped partial promoter-like regions had little effect on the phenotypes induced by wild-type betasatellites. Further experiments showed that the TYLCCNB-Y25 hybrid carrying the C-terminal region of TYLCCNB-Y10 βC1 induced TYLCCNB-Y10-like symptoms. These findings indicate that the βC1 protein is the major symptom determinant and that the C-terminal region of βC1 plays an important role in symptom induction

Deep Image: Scaling up Image Recognition

We present a state-of-the-art image recognition system, Deep Image, developed using end-to-end deep learning. The key components are a custom-built supercomputer dedicated to deep learning, a highly optimized parallel algorithm using new strategies for data partitioning and communication, larger deep neural network models, novel data augmentation approaches, and usage of multi-scale high-resolution images. Our method achieves excellent results on multiple challenging computer vision benchmarks.Comment: This paper has been withdrawn by the authors due to a mistake related to ImageNet server submission