Add Hub results.pandas() method (#2725)

* Add Hub results.pandas() method

New method converts results from torch tensors to pandas DataFrames with column names.

This PR may partially resolve issue https://github.com/ultralytics/yolov5/issues/2703

```python
results = model(imgs)

print(results.pandas().xyxy[0])
         xmin        ymin        xmax        ymax  confidence  class    name
0   57.068970  391.770599  241.383545  905.797852    0.868964      0  person
1  667.661255  399.303589  810.000000  881.396667    0.851888      0  person
2  222.878387  414.774231  343.804474  857.825073    0.838376      0  person
3    4.205386  234.447678  803.739136  750.023376    0.658006      5     bus
4    0.000000  550.596008   76.681190  878.669922    0.450596      0  person
```

* Update comments 

torch example input now shown resized to size=640 and also now a multiple of P6 stride 64 (see https://github.com/ultralytics/yolov5/issues/2722#issuecomment-814785930)

* apply decorators

* PEP8

* Update common.py

* pd.options.display.max_columns = 10

* Update common.py

hubconf.py

@@ -38,7 +38,7 @@ def create(name, pretrained, channels, classes, autoshape):
             fname = f'{name}.pt'  # checkpoint filename
             attempt_download(fname)  # download if not found locally
             ckpt = torch.load(fname, map_location=torch.device('cpu'))  # load
-            msd = model.state_dict()   # model state_dict
+            msd = model.state_dict()  # model state_dict
             csd = ckpt['model'].float().state_dict()  # checkpoint state_dict as FP32
             csd = {k: v for k, v in csd.items() if msd[k].shape == v.shape}  # filter
             model.load_state_dict(csd, strict=False)  # load