Cython should be a dependency of the remaining packages in requirements.txt, so should be installed anyway even if not a direct requirement.

This updates the default detect.py behavior to automatically save all inference images/videos/webcams unless the new argument --nosave is used (python detect.py --nosave) or unless a list of streaming sources is passed (python detect.py --source streams.txt)

guide describes YOLOv5 apps collection in Supervisely Ecosystem

Improved user-feedback following requirements auto-update.

Prints 'Please restart runtime or rerun command for update to take effect.' following package auto-install to inform users to restart/rerun.

* YOLOv5 PyTorch Hub models >> check_requirements()

Update YOLOv5 PyTorch Hub requirements.txt path to cache path.

* Update hubconf.py

* Update hubconf.py with check_requirements()

Dependency checks have been missing from YOLOv5 PyTorch Hub model loading, causing errors in some cases when users are attempting to import hub models in unsupported environments. This should examine the YOLOv5 requirements.txt file and pip install any missing or version-conflict packages encountered. 

This is highly experimental (!), please let us know if this creates problems in your custom workflows.

* Update hubconf.py

* Update check_requirements() with auto-install

This PR builds on an idea I had to automatically install missing dependencies rather than simply report an error message. 

YOLOv5 should now 1) display all dependency issues and not simply display the first missing dependency, and 2) attempt to install/update each missing/VersionConflict package.

* cleanup

* cleanup 2

* Check requirements.txt file exists

* cleanup 3

* PyTorch Hub models default to CUDA:0 if available

* device as string bug fix

* Add autoShape() speed profiling

* Update common.py

* Create README.md

* Update hubconf.py

* cleanuip

curl preferred over wget for slightly better cross platform compatibility (i.e. out of the box macos compatible).

* labels.png class names

* fontsize=10

* Update test.py --task train val study

* update argparser --task

* Integer printout

* test.py 'Labels'

* Update train.py

* added argoverse-download ability

* bugfix

* add support for Argoverse dataset

* Refactored code

* renamed to argoverse-HD

* unzip -q and YOLOv5

small cleanup items

* add image counts

Co-authored-by: Kartikeya Sharma <kartikes@trinity.vision.cs.cmu.edu>
Co-authored-by: Kartikeya Sharma <kartikes@trinity-0-32.eth>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* GCP sudo docker

* cleanup

* option for skip last layer and cuda export support

* added parameter device

* fix import

* cleanup 1

* cleanup 2

* opt-in grid

--grid will export with grid computation, default export will skip grid (same as current)

* default --device cpu

GPU export causes ONNX and CoreML errors.

Co-authored-by: Jan Hajek <jan.hajek@gmail.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

image weights compatible faster random index generator v2 for mosaic augmentation

* faster random index generator for mosaic augementation

We don't need to access list to generate random index

It makes augmentation slower.

* Update datasets.py

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Resume with custom anchors fix

* Update train.py

* EMA bug fix 2

* update