* handle exceptions| attempt CI

* update

* Pre-commit manual run

* yaml one-liner

* Update ci-testing.yml

* Comment W&B CI

Leave as example for future separate CI

* Update ci-testing.yml
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* AutoBatch, AutoAnchor `LOGGER`

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* Update autoanchor.py
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* Fix shuffle DataLoader argument

* Add shuffle argument

* Disable shuffle when rect

* Cleanup, add rect warning

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* Cleanup2

* Cleanup3
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* WORLD_SIZE-safe workers

* Update with DDP comment

@kalenmike per our convo yesterday.

* Improve plots.py robustness

Addresses issues #5374, #5395, #5611

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* `np.unique()` sort fix for segments

* Update datasets.py

Accepts all arguments now by default resolving https://github.com/ultralytics/yolov5/issues/5600

* Update tqdm for fixed width

* Update val.py

* Update val.py

* Try ncols= in train.py

* NCOLS

* NCOLS

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* bar_format

* position 0 leave true

* exp0

* auto

* auto

* Cleanup

* Cleanup

* Cleanup
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* Fix to resource warning allocation; utilize file.open within a context manager

* rename fh to f

in keeping with naming convention
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* Update __init__.py

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* notebook_init

* notebook_init

* notebook_init

* notebook_init

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* notebook_init

* Created using Colaboratory

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* New `DetectMultiBackend()` class

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* pb to pt fix

* Cleanup

* explicit apply_classifier path

* Cleanup2

* Cleanup3

* Cleanup4

* Cleanup5

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* val.py MultiBackend inference

* warmup fix

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* device fix

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* half fix

* detect fix

* detect fix 2

* remove half from DetectMultiBackend

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* training half handling 3

* Cleanup

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* Add torchscript _extra_files

* Add TorchScript

* Add CoreML

* CoreML cleanup

* New `DetectMultiBackend()` class

* pb to pt fix

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* Cleanup

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* Cleanup2

* Cleanup3

* Cleanup4

* Cleanup5

* Cleanup6

* val.py MultiBackend inference

* warmup fix

* to device fix

* pt fix

* device fix

* Val cleanup

* COCO128 URL to assets

* half fix

* detect fix

* detect fix 2

* remove half from DetectMultiBackend

* training half handling

* training half handling 2

* training half handling 3

* Cleanup

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* Add TorchScript

* Add CoreML

* CoreML cleanup

* revert default to pt

* Add Usage examples

* Cleanup val
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* Logger consolidation

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Partially addresses invalid mAPs at higher confidence threshold issue https://github.com/ultralytics/yolov5/issues/1466.

* Update detect.py Usage examples

* Inside Ultralytics at https://youtu.be/Zgi9g1ksQHc

* Fix `save_one_box()`

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Fix for Colab hub error:


```python
import yaml

with open('yolov5s.yaml', errors='ignore') as f:
     d = yaml.safe_load(f)  # model dict

print(d)

---------------------------------------------------------------------------
ReaderError                               Traceback (most recent call last)
<ipython-input-8-1150b5143073> in <module>()
      2 
      3 with open('yolov5s.yaml', errors='ignore') as f:
----> 4      d = yaml.safe_load(f)  # model dict
      5 
      6 print(d)

6 frames
/usr/local/lib/python3.7/dist-packages/yaml/reader.py in check_printable(self, data)
    142             position = self.index+(len(self.buffer)-self.pointer)+match.start()
    143             raise ReaderError(self.name, position, ord(character),
--> 144                     'unicode', "special characters are not allowed")
    145 
    146     def update(self, length):

ReaderError: unacceptable character #x1f680: special characters are not allowed
  in "yolov5s.yaml", position 9
```

* Update model yamls for v6.0

* Add python models/yolo.py --test

* Ghost fix

* Update bug-report.yml

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* Update bug-report.yml

* Update autobatch.py

* Update autobatch.py

* Update autobatch.py

Checking for `onnx_dynamic` first should suppress the warning:

```log
TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  if self.grid[i].shape[2:4] != x[i].shape[2:4] or self.onnx_dynamic
```

* correct --resume True error

* delete temp file

* Update train.py
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* fix export onnx bug

* Update export.py

* Update export.py

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* Update yolo.py
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* Fix detect.py URL inference

Allows detect.py to run inference on remote URL sources, i.e.:

```python
!python detect.py --weights yolov5s.pt --source https://ultralytics.com/assets/zidane.jpg  # image URL
!python detect.py --weights yolov5s.pt --source https://ultralytics.com/assets/decelera_landscape.mov  # video URL
```

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Resolves https://github.com/ultralytics/yolov5/pull/5341#issuecomment-961774729

Tests:
```python
import glob
import re
from pathlib import Path


def increment_path(path, exist_ok=False, sep='', mkdir=False):
    # Increment file or directory path, i.e. runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc.
    path = Path(path)  # os-agnostic
    if path.exists() and not exist_ok:
        path, suffix = (path.with_suffix(''), path.suffix) if path.is_file() else (path, '')
        dirs = glob.glob(f"{path}{sep}*")  # similar paths
        matches = [re.search(rf"%s{sep}(\d+)" % path.stem, d) for d in dirs]
        i = [int(m.groups()[0]) for m in matches if m]  # indices
        n = max(i) + 1 if i else 2  # increment number
        path = Path(f"{path}{sep}{n}{suffix}")  # increment path
    if mkdir:
        path.mkdir(parents=True, exist_ok=True)  # make directory
    return path


print(increment_path('runs'))
print(increment_path('export.py'))
print(increment_path('abc.def.dir'))
print(increment_path('abc.def.file'))
```

* Update cls bias init

Increased numerical precision. Returns 1.0 probability for single-class datasets now. Addresses https://github.com/ultralytics/yolov5/issues/5357

```python
torch.sigmoid(torch.tensor([math.log(0.6 / (1 - 0.99999))]))
Out[19]: tensor([1.0000])
```

* Update yolo.py

* Update plots.py

Error running python detect.py --visualize
#5503

* Update plots.py
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* write date in checkpoint file

write date in checkpoint file

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* isoformat
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Fix for https://github.com/ultralytics/yolov5/issues/5503
```
python detect.py --visualize --source path/to/file.suffix1.jpg
```

* precommit: isort

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* Update isort config

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* Update name
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* Handle edgetpu model inference

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* Cleanup

Rename `tflite_runtime.interpreter as tflite` to `tflite_runtime.interpreter as tflri` to avoid conflict with existing `tflite` boolean
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* Fix float zeros format

* 255 to integer

* Keras CI fix

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* Update tf.py with verified confirmation

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