Bumps [actions/cache](https://github.com/actions/cache) from 1 to 2.1.6.
- [Release notes](https://github.com/actions/cache/releases)
- [Commits](https://github.com/actions/cache/compare/v1...v2.1.6)

---
updated-dependencies:
- dependency-name: actions/cache
  dependency-type: direct:production
  update-type: version-update:semver-major
...
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

Bumps [cirrus-actions/rebase](https://github.com/cirrus-actions/rebase) from 1.4 to 1.5.
- [Release notes](https://github.com/cirrus-actions/rebase/releases)
- [Commits](https://github.com/cirrus-actions/rebase/compare/1.4...1.5)

---
updated-dependencies:
- dependency-name: cirrus-actions/rebase
  dependency-type: direct:production
  update-type: version-update:semver-minor
...
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

* Add `on_fit_epoch_end` callback

* Add results to train

* Update __init__.py

* Clip Objects365 autodownload labels (#5214)

Fixes out of bounds labels that seem to affect ~10% of images in dataset.

* Inplace ops

* Autofix duplicate labels

PR changes duplicate label handling from report error and ignore image-label pair to report warning and autofix image-label pair. 

This should fix this common issue for users and allow everyone to get started and get a model trained faster and easier than before.

* sign fix

* Cleanup

* Increment cache version

* all to any fix

* Update Objects365.yaml

Download the official Objects365 validation set and convert the labels

* Enforce 4-space indent, reformat and cleanup

* shorten list comprehension
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Add hyp.scratch-med.yaml

* Update hyp.scratch-med.yaml

* Highlight contributors in README

* Adjust Contact section

* Update README.md

* Improved check_suffix() robustness to `''` and `""`

* Cleanup

* legend-labels Adjust legend labels for classes without instances.

* #5158 Re-indexed series names: only classes containing data.

* #5158 Re-indexed series names: only classes containing data.

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

* fix dpp with wandb disabled

* PyCharm reformat

* Fix

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

* fix dpp with wandb disabled

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

* Add train class filter feature to datasets.py

Allows for training on a subset of total classes if `include_class` list is defined on datasets.py L448:
```python
        include_class = []  # filter labels to include only these classes (optional)
```

* segments fix

* Check `'onnxruntime-gpu' if torch.has_cuda`

* fix indent

* fix `tf` conversion in new v6 (#5147)

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

* Update P5 models

* Update P6 models

* Update with GFLOPs and Params

* Update with GFLOPs and Params

* Update README

* Update

* Update README

* Update

* Update

* Add times

* Update README

* Update results

* Update results

* Update results

* Update hyps

* Update plots

* Update plots

* Update README.md

* Add nano models to hubconf.py

* update detect.py in order to support torch script

This change assumes the torchscrip file was previously saved with `export.py`

* update `detect.py` for torchscript support

Simple update for torchscript support. Assumes the torchscript file has been generated with `export.py`

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

* Add OpenCV DNN option for ONNX inference

Usage:

```bash
python detect.py --weights yolov5s.onnx  # ONNX Runtime inference
python detect.py --weights yolov5s.onnx -dnn  # OpenCV DNN inference
```

* DNN prediction to tensor

* Update detect.py

* refactor anchors and anchor_grid in Detect Layer

* fix CI failures by adding compatibility

* fix tf failure

* fix different devices errors

* Cleanup

* fix anchors overwriting issue

* better refactoring

* Remove self.anchor_grid shape check (redundant with self.grid check)

Also PEP8 / 120 line width

* Convert _make_grid() from static to dynamic method

* Remove anchor_grid.to(device)

clone() should already clone to same device as self.anchors

* fix different devices error
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* use os.path.relpath instead of relative_to

* use os.path.relpath instead of relative_to

* Remove os.path from val.py

* Remove os.path from train.py

* Update detect.py import to os

* Update export.py import to os
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update val.py `pad = 0.0 if task == speed else 0.5`

* Cleanup

* fix different devices bug

* extend _apply() instead of to() for a general fix

* Only apply if Detect() is last layer
Co-authored-by: Jebastin Nadar <njebastin10@gmail.com>

* Indent fix

* Add comment to yolo.py

* Add comment to common.py
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

Co-authored-by: qiningonline <qiningonline@gmail.com>

* Requirements updated

1. Requests added to requirements.txt. That might not be included in all docker base images, adding it to the requirements is safer.
2. Added a minimum version to Pandas. It's a good practice to have versions for all dependencies.

* Sort alphabetically

* Update requirements.txt
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Re-open IP-camera videostream if disconnected. Provide IP-streaming analysis stability

* Re-open IP-camera videostream if disconnected. Provide IP-streaming analysis stability

* `self.imgs[i] *= 0` bug fix and cleanup
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update Dockerfile to `ADD` Arial.ttf

Fix for `Arial.ttf download in Docker` #5078

* Cleanup

* Cleanup2

This PR adds a new training argument `--save-period` to save training checkpoints every `x` epochs. To save training every 50 epochs for example:
```
python train.py --save-period 50  # saves epoch50.pt, epoch100.pt, epoch150.pt, ... etc.
```

This saved checkpoints in addition to existing last.pt and best.pt checkpoints and does not affect their behavior. Default value is -1, i.e. disabled.

* Clip TTA Augmented Tails

Experimental TTA update.

* Update yolo.py

* Update yolo.py

* Update yolo.py

* Update yolo.py