* Fix Indentation in test.py

* CI fix

* Comply with PEP8: 80 characters per line

* Update detections() self.t = tuple()

Fix multiple results.print() bug.

* Update experimental.py

* Update yolo.py

Updated device selection string with fallback for non-git directories.
```python
def select_device(device='', batch_size=None):
    # device = 'cpu' or '0' or '0,1,2,3'
    s = f'YOLOv5 🚀 {git_describe() or date_modified()} torch {torch.__version__} '  # string
...
```

Co-authored-by: Max Uppenkamp <max.uppenkamp@inform-software.com>

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

Revert unintentional change to test batch sizes caused by PR https://github.com/ultralytics/yolov5/pull/2125

* 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 tensorboard>=2.4.1 

Update tensorboard version to attempt to address https://github.com/ultralytics/yolov5/issues/2573 (tensorboard logging fail in Docker image).

* cleanup

* 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

Exclude non-critical packages from dependency checks in detect.py. pycocotools and thop in particular are not required for inference.

Issue first raised in https://github.com/ultralytics/yolov5/issues/1944 and also raised in https://github.com/ultralytics/yolov5/discussions/2556

Fix for results.tolist() method breaking after YOLOv5 Hub profiling PRshttps://github.com/ultralytics/yolov5/pull/2460 https://github.com/ultralytics/yolov5/pull/2459 and

* Init Commit

* new wandb integration

* Update

* Use data_dict in test

* Updates

* Update: scope of log_img

* Update: scope of log_img

* Update

* Update: Fix logging conditions

* Add tqdm bar, support for .txt dataset format

* Improve Result table Logger

* Init Commit

* new wandb integration

* Update

* Use data_dict in test

* Updates

* Update: scope of log_img

* Update: scope of log_img

* Update

* Update: Fix logging conditions

* Add tqdm bar, support for .txt dataset format

* Improve Result table Logger

* Add dataset creation in training script

* Change scope: self.wandb_run

* Add wandb-artifact:// natively

you can now use --resume with wandb run links

* Add suuport for logging dataset while training

* Cleanup

* Fix: Merge conflict

* Fix: CI tests

* Automatically use wandb config

* Fix: Resume

* Fix: CI

* Enhance: Using val_table

* More resume enhancement

* FIX : CI

* Add alias

* Get useful opt config data

* train.py cleanup

* Cleanup train.py

* more cleanup

* Cleanup| CI fix

* Reformat using PEP8

* FIX:CI

* rebase

* remove uneccesary changes

* remove uneccesary changes

* remove uneccesary changes

* remove unecessary chage from test.py

* FIX: resume from local checkpoint

* FIX:resume

* FIX:resume

* Reformat

* Performance improvement

* Fix local resume

* Fix local resume

* FIX:CI

* Fix: CI

* Imporve image logging

* (:(:Redo CI tests:):)

* Remember epochs when resuming

* Remember epochs when resuming

* Update DDP location

Potential fix for #2405

* PEP8 reformat

* 0.25 confidence threshold

* reset train.py plots syntax to previous

* reset epochs completed syntax to previous

* reset space to previous

* remove brackets

* reset comment to previous

* Update: is_coco check, remove unused code

* Remove redundant print statement

* Remove wandb imports

* remove dsviz logger from test.py

* Remove redundant change from test.py

* remove redundant changes from train.py

* reformat and improvements

* Fix typo

* Add tqdm tqdm progress when scanning files, naming improvements
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* PyTorch Hub models default to CUDA:0 if available

* device as string bug fix

Fix for https://github.com/ultralytics/yolov5/issues/2394

* Be able to create dataset from annotated images only

Add the ability to create a dataset/splits only with images that have an annotation file, i.e a .txt file, associated to it. As we talked about this, the absence of a txt file could mean two things:

* either the image wasn't yet labelled by someone,
* either there is no object to detect.

When it's easy to create small datasets, when you have to create datasets with thousands of images (and more coming), it's hard to track where you at and you don't want to wait to have all of them annotated before starting to train. Which means some images would lack txt files and annotations, resulting in label inconsistency as you say in #2313. By adding the annotated_only argument to the function, people could create, if they want to, datasets/splits only with images that were labelled, for sure.

* Cleanup and update print()
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* 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>