MrSupW / datasetapi
规范化管理labelme数据集并生成coco数据集
☆85Updated 4 years ago
Alternatives and similar repositories for datasetapi:
Users that are interested in datasetapi are comparing it to the libraries listed below
- 这是一个retinanet-pytorch的源码,可以用于训练自己的模型。☆180Updated last year
- 这里面存放了一些目标检测算法的数据增强方法。如mosaic、mixup。☆157Updated 2 years ago
- Trans DOTA OBB format(poly format) to YOLO format.☆205Updated 3 years ago
- ☆106Updated 2 years ago
- 本仓库主要包含了针对目标检测数据集的增强手段和源码:图像的旋转,镜像,裁剪,亮度/对比度的变换等☆132Updated 4 years ago
- ☆76Updated 3 years ago
- annotations of yolov5-5.0☆232Updated 3 years ago
- 目标检测数据集制作:VOC,COCO,YOLO等常用数据集格式的制作和互相转换脚本☆436Updated 3 years ago
- ☆312Updated last year
- ☆68Updated 3 years ago
- 这是一个centernet-pytorch的源码,可以用于训练自己的模型。☆374Updated last year
- Pytorch implementation of the 'Slim-neck by GSConv: a lightweight-design for real-time detector architectures'☆205Updated 4 months ago
- Latest paper about small object detection☆431Updated last year
- Yolov5 distillation training | Yolov5知识蒸馏训练,支持训练自己的数据☆211Updated 2 years ago
- ☆139Updated 2 years ago
- yolov5 5.0 version distillation || yolov5 5.0版本知识蒸馏,yolov5l >> yolov5s☆157Updated 3 years ago
- 以Swin Transformer作为骨干网络的YoloX目标检测项目☆78Updated 2 years ago
- Multi-backbone, Prune, Quantization, KD☆158Updated 2 years ago
- ☁️💡🎈专注于改进YOLOv7,Support to improve Backbone, Neck, Head, Loss, IoU, NMS and other modules☆203Updated 10 months ago
- 这是一个efficientdet-pytorch的源码,可以用于训练自己的模型。☆313Updated last year
- 一个修改YOLOv5以使用SwinTransformer模块的代码仓库。A repository that modifies YOLOv5 to use various SwinTransformer blocks.☆109Updated 3 months ago
- ImgEnhance For Obejct Detection tool☆133Updated 5 years ago
- Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression (AAAI 2020)☆47Updated 3 years ago
- ☆169Updated last year
- 这是一个efficientnet-yolo3-pytorch的源码,将yolov3的主干特征提取网络修改成了efficientnet☆146Updated last year
- YOLOV5 小目标检测修改版☆184Updated 3 years ago
- rotation detection based on yolov5☆376Updated last year
- 这是一个yolov5-v6.1-pytorch的源码,可以用于训练自己的模型。☆121Updated last year
- Learning YOLOv3 from scratch 从零开始学习YOLOv3代码