YOLOv7: YOLO with Transformers and Instance Segmentation, with TensorRT acceleration!

GitHub – jinfagang/yolov7: 🔥🔥🔥🔥 YOLO with Transformers and Instance Segmentation, with TensorRT acceleration! 🔥🔥🔥

“In short: YOLOv7 added instance segmentation to YOLO arch. Also many transformer backbones, archs included. If you look carefully, you’ll find our ultimate vision is to make YOLO great again by the power of transformers, as well as multi-tasks training. YOLOv7 achieves mAP 43, AP-s exceed MaskRCNN by 10 with a convnext-tiny backbone while simillar speed with YOLOX-s, more models listed below, it’s more accurate and even more lighter!

Just another yolo variant implemented based on detectron2. But note that YOLOv7 doesn’t meant to be a successor of yolo family, 7 is just a magic and lucky number. Instead, YOLOv7 extend yolo into many other vision tasks, such as instance segmentation, one-stage keypoints detection etc..

The supported matrix in YOLOv7 are:

•  YOLOv4 contained with CSP-Darknet53;
•  YOLOv7 arch with resnets backbone;
•  YOLOv7 arch with resnet-vd backbone (likely as PP-YOLO), deformable conv, Mish etc;
•  GridMask augmentation from PP-YOLO included;
•  Mosiac transform supported with a custom datasetmapper;
•  YOLOv7 arch Swin-Transformer support (higher accuracy but lower speed);
•  YOLOv7 arch Efficientnet + BiFPN;
•  YOLOv5 style positive samples selection, new coordinates coding style;
•  RandomColorDistortion, RandomExpand, RandomCrop, RandomFlip;
•  CIoU loss (DIoU, GIoU) and label smoothing (from YOLOv5 & YOLOv4);
•  YOLOF also included;
•  YOLOv7 Res2net + FPN supported;
•  Pyramid Vision Transformer v2 (PVTv2) supported;
•  WBF (Weighted Box Fusion), this works better than NMS, link;
•  YOLOX like head design and anchor design, also training support;
•  YOLOX s,m,l backbone and PAFPN added, we have a new combination of YOLOX backbone and pafpn;
•  YOLOv7 with Res2Net-v1d backbone, we found res2net-v1d have a better accuracy then darknet53;
•  Added PPYOLOv2 PAN neck with SPP and dropblock;
•  YOLOX arch added, now you can train YOLOX model (anchor free yolo) as well;
•  DETR: transformer based detection model and onnx export supported, as well as TensorRT acceleration;
•  AnchorDETR: Faster converge version of detr, now supported!
•  Almost all models can export to onnx;
•  Supports TensorRT deployment for DETR and other transformer models;
•  It will integrate with wanwu, a torch-free deploy framework run fastest on your target platform…”

Source: github.com/jinfagang/yolov7