Can we combine different yolov5 models trained of different classes into one? Suppose I've a yolov5 model trained on cars and second trained on bus and third trained on bike and so on. Is there a way through which I can combine all the model into a single model?
As by running all model it will take extra time.

The process of using two or more models for inferencing one single data is carried out under model ensembling in YoloV5.
Model Ensembling Tutorial  clearly defines:

Ensemble modeling is a process where multiple diverse models are created to predict an outcome, either by using many different modeling algorithms or using different training data sets. The ensemble model then aggregates the prediction of each base model and results in once final prediction for the unseen data. The motivation for using ensemble models is to reduce the generalization error of the prediction.

We can use:
python detect.py --weights yolov5x.pt yolov5l6.pt --data coco.yaml --img 640 --other-arguments

But the two or more models should be trained for the same classes only. Model ensembling can improve mAP and Recall during testing and inference.
As clarified in issue#1188
