Related: 250115-1238 Adding wandb to a CLI yolo run

• Reference are surprisingly hard to find on the website: results - Ultralytics YOLO Docs

• yolo detect train model=yolo11s.pt data=/data/data/data.yaml project=/data/project/ epochs=500 imgsz=640 device=0,1 name=yolo11s-aug-500epochs-full

• YOLOv11 sets default batch_size 16, one can set -1 for it to automatically pick one that’s 60% of GPU, or 0.8 to automatically pick one that’s 80% of GPU

• To decrease verbosity in predictions, verbose=False to model.predict() (and `.track()) works¹.

• Changing imgsz= to something lower may not necessarily make it faster, if a model was trained with a certain size it may predict faster at that size (e.g. OSCF/TrapperAI-v02.2024 predicts at 40+ iterations per second when resized to 640 and ~31 when left to its default 1024pd)

  • Resizing (if provided a single int, not a tuple) works by making the larger side of the image equal to the given one, if padding is needed grey is used(?)

• Half-life precision (if supported by GPU) is really cool! half=True makes stuff faster (no idea about prediction quality yet)

  • And batch size obviously

• vid_stride predicts every Nth video frame, was almost going to write that myself

All-in-all I like ultralytics/YOLO