Replace Docs URLs with relative links (#11738)

docs/en/yolov5/tutorials/multi_gpu_training.md

@@ -16,7 +16,7 @@ cd yolov5
 pip install -r requirements.txt  # install
 ```
 
-💡 ProTip! **Docker Image** is recommended for all Multi-GPU trainings. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
+💡 ProTip! **Docker Image** is recommended for all Multi-GPU trainings. See [Docker Quickstart Guide](../environments/docker_image_quickstart_tutorial.md) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
 
 💡 ProTip! `torch.distributed.run` replaces `torch.distributed.launch` in **PyTorch>=1.9**. See [docs](https://pytorch.org/docs/stable/distributed.html) for details.
 
@@ -121,7 +121,7 @@ python -m torch.distributed.run --master_port 1234 --nproc_per_node 2 ...
 
 ## Results
 
-DDP profiling results on an [AWS EC2 P4d instance](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/) with 8x A100 SXM4-40GB for YOLOv5l for 1 COCO epoch.
+DDP profiling results on an [AWS EC2 P4d instance](../environments/aws_quickstart_tutorial.md) with 8x A100 SXM4-40GB for YOLOv5l for 1 COCO epoch.
 
 <details>
   <summary>Profiling code</summary>

docs/en/yolov5/tutorials/pytorch_hub_model_loading.md

@@ -195,7 +195,7 @@ threading.Thread(target=run, args=[model1, 'https://ultralytics.com/images/bus.j
 
 ### Training
 
-To load a YOLOv5 model for training rather than inference, set `autoshape=False`. To load a model with randomly initialized weights (to train from scratch) use `pretrained=False`. You must provide your own training script in this case. Alternatively see our YOLOv5 [Train Custom Data Tutorial](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data) for model training.
+To load a YOLOv5 model for training rather than inference, set `autoshape=False`. To load a model with randomly initialized weights (to train from scratch) use `pretrained=False`. You must provide your own training script in this case. Alternatively see our YOLOv5 [Train Custom Data Tutorial](./train_custom_data.md) for model training.
 
 ```python
 import torch
@@ -338,7 +338,7 @@ model = torch.hub.load('path/to/yolov5', 'custom', path='path/to/best.pt', sourc
 
 ## TensorRT, ONNX and OpenVINO Models
 
-PyTorch Hub supports inference on most YOLOv5 export formats, including custom trained models. See [TFLite, ONNX, CoreML, TensorRT Export tutorial](https://docs.ultralytics.com/yolov5/tutorials/model_export) for details on exporting models.
+PyTorch Hub supports inference on most YOLOv5 export formats, including custom trained models. See [TFLite, ONNX, CoreML, TensorRT Export tutorial](./model_export.md) for details on exporting models.
 
 💡 ProTip: **TensorRT** may be up to 2-5X faster than PyTorch on [**GPU benchmarks**](https://github.com/ultralytics/yolov5/pull/6963)
 💡 ProTip: **ONNX** and **OpenVINO** may be up to 2-3X faster than PyTorch on [**CPU benchmarks**](https://github.com/ultralytics/yolov5/pull/6613)

docs/en/yolov5/tutorials/tips_for_best_training_results.md

@@ -19,7 +19,7 @@ We've put together a full guide for users looking to get the best results on the
 - **Image variety.** Must be representative of deployed environment. For real-world use cases we recommend images from different times of day, different seasons, different weather, different lighting, different angles, different sources (scraped online, collected locally, different cameras) etc.
 - **Label consistency.** All instances of all classes in all images must be labelled. Partial labelling will not work.
 - **Label accuracy.** Labels must closely enclose each object. No space should exist between an object and it's bounding box. No objects should be missing a label.
-- **Label verification.** View `train_batch*.jpg` on train start to verify your labels appear correct, i.e. see [example](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data#local-logging) mosaic.
+- **Label verification.** View `train_batch*.jpg` on train start to verify your labels appear correct, i.e. see [example](./train_custom_data.md#local-logging) mosaic.
 - **Background images.** Background images are images with no objects that are added to a dataset to reduce False Positives (FP). We recommend about 0-10% background images to help reduce FPs (COCO has 1000 background images for reference, 1% of the total). No labels are required for background images.
 
 <a href="https://arxiv.org/abs/1405.0312"><img width="800" src="https://user-images.githubusercontent.com/26833433/109398377-82b0ac00-78f1-11eb-9c76-cc7820669d0d.png" alt="COCO Analysis"></a>
@@ -56,7 +56,7 @@ Before modifying anything, **first train with default settings to establish a pe
 - **Epochs.** Start with 300 epochs. If this overfits early then you can reduce epochs. If overfitting does not occur after 300 epochs, train longer, i.e. 600, 1200 etc. epochs.
 - **Image size.** COCO trains at native resolution of `--img 640`, though due to the high amount of small objects in the dataset it can benefit from training at higher resolutions such as `--img 1280`. If there are many small objects then custom datasets will benefit from training at native or higher resolution. Best inference results are obtained at the same `--img` as the training was run at, i.e. if you train at `--img 1280` you should also test and detect at `--img 1280`.
 - **Batch size.** Use the largest `--batch-size` that your hardware allows for. Small batch sizes produce poor batchnorm statistics and should be avoided.
-- **Hyperparameters.** Default hyperparameters are in [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml). We recommend you train with default hyperparameters first before thinking of modifying any. In general, increasing augmentation hyperparameters will reduce and delay overfitting, allowing for longer trainings and higher final mAP. Reduction in loss component gain hyperparameters like `hyp['obj']` will help reduce overfitting in those specific loss components. For an automated method of optimizing these hyperparameters, see our [Hyperparameter Evolution Tutorial](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution).
+- **Hyperparameters.** Default hyperparameters are in [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml). We recommend you train with default hyperparameters first before thinking of modifying any. In general, increasing augmentation hyperparameters will reduce and delay overfitting, allowing for longer trainings and higher final mAP. Reduction in loss component gain hyperparameters like `hyp['obj']` will help reduce overfitting in those specific loss components. For an automated method of optimizing these hyperparameters, see our [Hyperparameter Evolution Tutorial](./hyperparameter_evolution.md).
 
 ## Further Reading
 

docs/en/yolov5/tutorials/train_custom_data.md

@@ -161,7 +161,7 @@ export COMET_API_KEY=<Your API Key>  # 2. paste API key
 python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt  # 3. train
 ```
 
-To learn more about all the supported Comet features for this integration, check out the [Comet Tutorial](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration). If you'd like to learn more about Comet, head over to our [documentation](https://bit.ly/yolov5-colab-comet-docs). Get started by trying out the Comet Colab Notebook: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)
+To learn more about all the supported Comet features for this integration, check out the [Comet Tutorial](./comet_logging_integration.md). If you'd like to learn more about Comet, head over to our [documentation](https://bit.ly/yolov5-colab-comet-docs). Get started by trying out the Comet Colab Notebook: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)
 
 <img width="1920" alt="YOLO UI" src="https://user-images.githubusercontent.com/26833433/202851203-164e94e1-2238-46dd-91f8-de020e9d6b41.png">
 
@@ -174,7 +174,7 @@ To learn more about all the supported Comet features for this integration, check
 
 You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).
 
-You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) for details!
+You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](./clearml_logging_integration.md) for details!
 
 <a href="https://clear.ml/">
 <img alt="ClearML Experiment Management UI" src="https://github.com/thepycoder/clearml_screenshots/raw/main/scalars.jpg" width="1280"></a>
@@ -201,10 +201,10 @@ plot_results('path/to/results.csv')  # plot 'results.csv' as 'results.png'
 
 Once your model is trained you can use your best checkpoint `best.pt` to:
 
-- Run [CLI](https://github.com/ultralytics/yolov5#quick-start-examples) or [Python](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) inference on new images and videos
+- Run [CLI](https://github.com/ultralytics/yolov5#quick-start-examples) or [Python](./pytorch_hub_model_loading.md) inference on new images and videos
 - [Validate](https://github.com/ultralytics/yolov5/blob/master/val.py) accuracy on train, val and test splits
-- [Export](https://docs.ultralytics.com/yolov5/tutorials/model_export) to TensorFlow, Keras, ONNX, TFlite, TF.js, CoreML and TensorRT formats
-- [Evolve](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution) hyperparameters to improve performance
+- [Export](./model_export.md) to TensorFlow, Keras, ONNX, TFlite, TF.js, CoreML and TensorRT formats
+- [Evolve](./hyperparameter_evolution.md) hyperparameters to improve performance
 - [Improve](https://docs.roboflow.com/adding-data/upload-api?ref=ultralytics) your model by sampling real-world images and adding them to your dataset
 
 ## Supported Environments