* Introduce hybrid auto-labelling support

* cleanup

* YOLOv5 forward compatibility update

* add data dir

* ci test yolov3

* update build_targets()

* update build_targets()

* update build_targets()

* update yolov3-spp.yaml

* add yolov3-tiny.yaml

* add yolov3-tiny.yaml

* Update yolov3-tiny.yaml

* thop bug fix

* Detection() device bug fix

* Use torchvision.ops.nms()

* Remove redundant download mirror

* CI tests with yolov3-tiny

* Update README.md

* Synch train and test iou_thresh

* update requirements.txt

* Cat apriori autolabels

* Confusion matrix

* Autosplit

* Autosplit

* Update README.md

* AP no plot

* Update caching

* Update caching

* Caching bug fix

* --image-weights bug fix

* datasets bug fix

* mosaic plots bug fix

* plot_study

* boxes.max()

* boxes.max()

* boxes.max()

* boxes.max()

* boxes.max()

* boxes.max()

* update

* Update README

* Update README

* Update README.md

* Update README.md

* results png

* Update README

* Targets scaling bug fix

* update plot_study

* update plot_study

* update plot_study

* update plot_study

* Targets scaling bug fix

* Finish Readme.md

* Finish Readme.md

* Finish Readme.md

* Update README.md

* Creado con Colaboratory

Co-authored-by: NPriteshkumar Bharatbhai Gohil <pgohil@assystemtechnologies.com>

* faster and more informative training plots

* Update utils.py

Looks good. Needs pep8 linting, I'll do that in PyCharm later once PR is in.

* Update test.py

* Update train.py

f for the tb descriptor lets us plot several batches, i.e. to allow us to change L292 to 'if ni < 3' for 3 examples.
Co-authored-by: NGlenn Jocher <glenn.jocher@ultralytics.com>