* Improved dataset error introspection

Intended to help https://github.com/ultralytics/yolov5/issues/8090

* Update general.py

* Apple Metal Performance Shader (MPS) device support

Following https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/

Should work with Apple M1 devices with PyTorch nightly installed with command `--device mps`. Usage examples:
```bash
python train.py --device mps
python detect.py --device mps
python val.py --device mps
```

* Update device strategy to fix MPS issue

Resolves https://github.com/ultralytics/yolov5/discussions/7931

* Code refactor for general.py

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* Cleanup

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Fix for https://github.com/ultralytics/yolov5/discussions/7901

* Improve mAP0.5-0.95

Two changes provided
1. Added limit on the maximum number of detections for each image likewise pycocotools
2. Rework process_batch function

Changes #2 solved issue #4251
I also independently encountered the problem described in issue #4251 that the values for the same thresholds do not match when changing the limits in the torch.linspace function.
These changes solve this problem.

Currently during validation yolov5x.pt model the following results were obtained:
from yolov5 validation
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100%|██████████| 157/157 [01:07<00:00,  2.33it/s]
                 all       5000      36335      0.743      0.626      0.682      0.506
from pycocotools
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.505
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.685

These results are very close, although not completely pass the competition issue #2258.
I think it's problem with false positive bboxes matched ignored criteria, but this is not actual for custom datasets and does not require an additional solution.

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* Remove line to retain pycocotools results

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* Create docker.yml

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* Update Dockerfile-cpu `--no-install-recommends`

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* add random_interpolation option to make model robust to interpolation methods

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@sergiossm

* Pin downloads to release version

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* FROM nvcr.io/nvidia/pytorch:22.04-py3

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fix bug in issue #7650

Improved robustness to filename edge cases like `data/images/zidane.23.jpg` that currently fail. May resolve https://github.com/ultralytics/yolov5/issues/7432

* PyTorch Hub `_verbose=False` fix2

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* Add redundant weights download mirrors

This PR seeks to improve access worldwide to YOLOv5 weights. Universal access to AI for all is our core value, and we are against any censorship or restriction efforts.

I've uploaded the official YOLOv5 v6.1 weights to a primary backup bucket on GCP and to secondary backup on Google Drive at https://drive.google.com/drive/folders/1EFQTEUeXWSFww0luse2jB9M1QNZQGwNl. Autodownload with try the first two locations (GitHuB release assets and GCP bucket), and point users to the Google Drive folder if the first two fail.

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* support nomedia

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* Update train.py

* Revert no plot evolve

evolve plots do not contain any images

* Revert plot_results

contains no media

* Update wandb_utils.py

* sync-bn cleanup

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* Rename nomedia -> noplots

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