Update links to https://docs.ultralytics.com (#11412)

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README.md

@@ -85,7 +85,7 @@ pip install -r requirements.txt  # install
 <details>
 <summary>Inference</summary>
 
-YOLOv5 [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest
+YOLOv5 [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest
 YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
 
 ```python
@@ -134,7 +134,7 @@ The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5
 results. [Models](https://github.com/ultralytics/yolov5/tree/master/models)
 and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest
 YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are
-1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://github.com/ultralytics/yolov5/issues/475) times faster). Use the
+1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training) times faster). Use the
 largest `--batch-size` possible, or pass `--batch-size -1` for
 YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB.
 
@@ -247,7 +247,7 @@ YOLOv5 has been designed to be super easy to get started and simple to learn. We
 - All checkpoints are trained to 300 epochs with default settings. Nano and Small models use [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, all others use [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml).
 - **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.<br>Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
 - **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) instance. NMS times (~1 ms/img) not included.<br>Reproduce by `python val.py --data coco.yaml --img 640 --task speed --batch 1`
-- **TTA** [Test Time Augmentation](https://github.com/ultralytics/yolov5/issues/303) includes reflection and scale augmentations.<br>Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
+- **TTA** [Test Time Augmentation](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation) includes reflection and scale augmentations.<br>Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
 
 </details>
 
@@ -484,4 +484,4 @@ For YOLOv5 bug reports and feature requests please visit [GitHub Issues](https:/
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="" /></a>
 </div>
 
-[tta]: https://github.com/ultralytics/yolov5/issues/303
+[tta]: https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation