# yolov7 **Repository Path**: sky313947/yolov7 ## Basic Information - **Project Name**: yolov7 - **Description**: aaaaaaaaaaaaaaaaaaa - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: v7_mask - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 1 - **Forks**: 0 - **Created**: 2022-09-14 - **Last Updated**: 2022-11-04 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # yolov7 Implementation of "YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors" This implimentation is based on [yolov5](https://github.com/ultralytics/yolov5). All of installation, data preparation, and usage are as same as yolov5. ## Training ``` shell python segment/train.py --data coco.yaml --batch 16 --weights '' --cfg yolov7-seg.yaml --epochs 300 --name yolov7-seg --img 640 --hyp hyp.scratch-high.yaml ``` ## Results [`yolov7-seg.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-seg.pt) ``` Object detection: Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.49629 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.67746 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.53842 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.32679 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.55475 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.63948 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.37569 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.61747 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.66796 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.49381 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.72859 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.81632 Instance segmentation: Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.40531 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.64003 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.42996 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.22329 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.46102 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.56453 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.32220 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.51069 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.54511 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.34929 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.61070 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.72177 ``` [`yolov7x-seg.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x-seg.pt) ``` Object detection: Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.51650 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.69517 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.56113 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.34761 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.57255 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.66059 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.38840 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.63697 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.68754 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.51488 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.74506 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.83554 Instance segmentation: Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.41901 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.65833 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.44483 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.23123 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.47414 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.58149 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.33164 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.52327 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.55738 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.35367 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.62091 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.74205 ``` ## Examples