Update cls bias init (#5520)

* 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

models/yolo.py

@@ -201,7 +201,7 @@ class Model(nn.Module):
         for mi, s in zip(m.m, m.stride):  # from
             b = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
             b.data[:, 4] += math.log(8 / (640 / s) ** 2)  # obj (8 objects per 640 image)
-            b.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum())  # cls
+            b.data[:, 5:] += math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # cls
             mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
 
     def _print_biases(self):