Update Colab Notebook CI

[glenn-jocher]

# CI Checks
# export PYTHONPATH="$PWD"  # to run *.py. files in subdirectories
rm -rf runs  # remove runs/
m=yolov5n  # official weights
b=runs/train/exp/weights/best  # best.pt weights
python train.py --img 64 --batch 32 --weights $m.pt --cfg $m.yaml --epochs 1 --device 0  # train
for d in 0 cpu; do  # devices
  python val.py --img 64 --batch 32 --weights $m.pt --device $d  # val official
  python val.py --img 64 --batch 32 --weights $b.pt --device $d  # val custom
  python detect.py --weights $m.pt --device $d  # detect official
  python detect.py --weights $b.pt --device $d  # detect custom
done
python hubconf.py  # hub
python models/tf.py --weights $m.pt  # build TF model
python models/yolo.py --cfg $m.yaml  # build PyTorch model
python export.py --weights $m.pt --img 64 --include torchscript  # export

OR

# CI Checks
# export PYTHONPATH="$PWD"  # to run *.py. files in subdirectories
rm -rf runs  # remove runs/
m=yolov5n  # official weights
b=runs/train/exp/weights/best  # best.pt checkpoint
python train.py --imgsz 64 --batch 32 --weights $m.pt --cfg $m.yaml --epochs 1 --device 0  # train
for d in 0 cpu; do  # devices
  for w in $m $b; do  # weights
    python val.py --imgsz 64 --batch 32 --weights $w.pt --device $d  # val
    python detect.py --imgsz 64 --weights $w.pt --device $d  # detect
  done
done
python hubconf.py --model $m  # hub
python models/tf.py --weights $m.pt  # build TF model
python models/yolo.py --cfg $m.yaml  # build PyTorch model
python export.py --weights $m.pt --img 64 --include torchscript  # export

🛠️ PR Summary

_{Made with ❤️ by Ultralytics Actions}

🌟 Summary

Refinements to YOLOv5's tutorial notebook and CI scripts for better clarity and efficiency.

📊 Key Changes

• Cleared execution_count in the tutorial Jupyter notebook, making it cleaner to new users.
• Simplified continual integration (CI) scripts by streamlining the model training and evaluation commands.
• Updated section titles and removed redundant code in the CI scripts for better readability.

🎯 Purpose & Impact

• The removal of execution_count ensures that the notebook looks fresh and unexecuted for each new user. 👩‍🏫
• The streamlined CI scripts enhance the maintainability of the code and the efficiency of the testing process. 🛠️
• Neater CI scripts and comments improve the understandability of automated processes for contributors and maintainers. 📝

The modifications are expected to make the learning and development process with YOLOv5 more accessible and efficient for users and developers alike. 🚀