Evaluation of 'best' and 'last' models will use the same params as the evaluation during the training phase. This PR fixes https://github.com/ultralytics/yolov5/issues/3907modifyDataloader
@@ -457,8 +457,6 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary | |||
results, _, _ = test.run(data_dict, | |||
batch_size=batch_size // WORLD_SIZE * 2, | |||
imgsz=imgsz_test, | |||
conf_thres=0.001, | |||
iou_thres=0.7, | |||
model=attempt_load(m, device).half(), | |||
single_cls=single_cls, | |||
dataloader=testloader, |