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@ -65,6 +65,8 @@ You will get the results:
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.83868 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.83868
``` ```
To measure accuracy, download [COCO-annotations for Pycocotools](http://images.cocodataset.org/annotations/annotations_trainval2017.zip).
## Training ## Training
``` ```
@ -81,6 +83,9 @@ python -m torch.distributed.launch --nproc_per_node 4 --master_port 9527 train.p
The training code and instruction of p6 models will release soon. The training code and instruction of p6 models will release soon.
Download MS COCO dataset images ([train](http://images.cocodataset.org/zips/train2017.zip), [val](http://images.cocodataset.org/zips/val2017.zip), [test](http://images.cocodataset.org/zips/test2017.zip)) and [labels](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/coco2017labels-segments.zip). If you have previously used a different version of YOLO, we strongly recommend that you delete `train2017.cache` and `val2017.cache` files, and redownload [labels](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/coco2017labels-segments.zip)
## Re-parameterization ## Re-parameterization
The re-parameterization code and instruction will release soon. The re-parameterization code and instruction will release soon.