ultralytics 8.0.91 tracker fix and docs comments (#2343)

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Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>

docs/yolov5/environments/aws_quickstart_tutorial.md

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 # YOLOv5 🚀 on AWS Deep Learning Instance: A Comprehensive Guide
 
 This guide will help new users run YOLOv5 on an Amazon Web Services (AWS) Deep Learning instance. AWS offers a [Free Tier](https://aws.amazon.com/free/) and a [credit program](https://aws.amazon.com/activate/) for a quick and affordable start. 

docs/yolov5/environments/docker_image_quickstart_tutorial.md

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 # Get Started with YOLOv5 🚀 in Docker
 
 This tutorial will guide you through the process of setting up and running YOLOv5 in a Docker container. 

docs/yolov5/environments/google_cloud_quickstart_tutorial.md

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 # Run YOLOv5 🚀 on Google Cloud Platform (GCP) Deep Learning Virtual Machine (VM) ⭐
 
 This tutorial will guide you through the process of setting up and running YOLOv5 on a GCP Deep Learning VM. New GCP users are eligible for a [$300 free credit offer](https://cloud.google.com/free/docs/gcp-free-tier#free-trial). 

docs/yolov5/index.md

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-# YOLOv5 Docs
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+# Ultralytics YOLOv5
 
 <div align="center">
   <p>

docs/yolov5/quickstart_tutorial.md

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 # YOLOv5 Quickstart
 
 See below for quickstart examples.

docs/yolov5/tutorials/architecture_description.md

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 ## 1. Model Structure
 
 YOLOv5 (v6.0/6.1) consists of:

docs/yolov5/tutorials/clearml_logging_integration.md

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 # ClearML Integration
 
 <img align="center" src="https://github.com/thepycoder/clearml_screenshots/raw/main/logos_dark.png#gh-light-mode-only" alt="Clear|ML"><img align="center" src="https://github.com/thepycoder/clearml_screenshots/raw/main/logos_light.png#gh-dark-mode-only" alt="Clear|ML">

docs/yolov5/tutorials/comet_logging_integration.md

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 <img src="https://cdn.comet.ml/img/notebook_logo.png">
 
 # YOLOv5 with Comet

docs/yolov5/tutorials/hyperparameter_evolution.md

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 📚  This guide explains **hyperparameter evolution** for YOLOv5 🚀. Hyperparameter evolution is a method of [Hyperparameter Optimization](https://en.wikipedia.org/wiki/Hyperparameter_optimization) using a [Genetic Algorithm](https://en.wikipedia.org/wiki/Genetic_algorithm) (GA) for optimization. UPDATED 25 September 2022.
 
 Hyperparameters in ML control various aspects of training, and finding optimal values for them can be a challenge. Traditional methods like grid searches can quickly become intractable due to 1) the high dimensional search space 2) unknown correlations among the dimensions, and 3) expensive nature of evaluating the fitness at each point, making GA a suitable candidate for hyperparameter searches.

docs/yolov5/tutorials/model_ensembling.md

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 📚  This guide explains how to use YOLOv5 🚀 **model ensembling** during testing and inference for improved mAP and Recall.  
 UPDATED 25 September 2022.
 

docs/yolov5/tutorials/model_export.md

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 # TFLite, ONNX, CoreML, TensorRT Export
 
 📚 This guide explains how to export a trained YOLOv5 🚀 model from PyTorch to ONNX and TorchScript formats.  

docs/yolov5/tutorials/model_pruning_and_sparsity.md

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 📚 This guide explains how to apply **pruning** to YOLOv5 🚀 models.  
 UPDATED 25 September 2022.
 

docs/yolov5/tutorials/multi_gpu_training.md

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 📚 This guide explains how to properly use **multiple** GPUs to train a dataset with YOLOv5 🚀 on single or multiple machine(s).  
 UPDATED 25 December 2022.
 

docs/yolov5/tutorials/neural_magic_pruning_quantization.md

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 <!--
 Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
 
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 - **Infinite Scalability**: Scale vertically to 100s of cores, out with standard Kubernetes, or fully-abstracted with Serverless
 - **Easy Integration**: Clean APIs for integrating your model into an application and monitoring it in production
 
-**[Start your 90 day Free Trial](https://neuralmagic.com/deepsparse-free-trial/?utm_campaign=free_trial&utm_source=ultralytics_github).**
-
 ### How Does DeepSparse Achieve GPU-Class Performance?
 
 DeepSparse takes advantage of model sparsity to gain its performance speedup. 
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 ## Get Started With DeepSparse
 
 **Research or Testing?** DeepSparse Community is free for research and testing. Get started with our [Documentation](https://docs.neuralmagic.com/).
-
-**Want to Try DeepSparse Enterprise?** [Start your 90 day free trial](https://neuralmagic.com/deepsparse-free-trial/?utm_campaign=free_trial&utm_source=ultralytics_github).

docs/yolov5/tutorials/pytorch_hub_model_loading.md

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 📚 This guide explains how to load YOLOv5 🚀 from PyTorch Hub  at [https://pytorch.org/hub/ultralytics_yolov5](https://pytorch.org/hub/ultralytics_yolov5).  
 UPDATED 26 March 2023.
 

docs/yolov5/tutorials/roboflow_datasets_integration.md

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 # Roboflow Datasets
 
 You can now use Roboflow to organize, label, prepare, version, and host your datasets for training YOLOv5 🚀 models. Roboflow is free to use with YOLOv5 if you make your workspace public.  

docs/yolov5/tutorials/running_on_jetson_nano.md

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 # Deploy on NVIDIA Jetson using TensorRT and DeepStream SDK
 
 📚 This guide explains how to deploy a trained model into NVIDIA Jetson Platform and perform inference using TensorRT and DeepStream SDK. Here we use TensorRT to maximize the inference performance on the Jetson platform.  

docs/yolov5/tutorials/test_time_augmentation.md

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 # Test-Time Augmentation (TTA)
 
 📚 This guide explains how to use Test Time Augmentation (TTA) during testing and inference for improved mAP and Recall with YOLOv5 🚀.  

docs/yolov5/tutorials/tips_for_best_training_results.md

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 📚 This guide explains how to produce the best mAP and training results with YOLOv5 🚀.  
 UPDATED 25 May 2022.
 

docs/yolov5/tutorials/train_custom_data.md

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 📚 This guide explains how to train your own **custom dataset** with [YOLOv5](https://github.com/ultralytics/yolov5) 🚀.  
 UPDATED 26 March 2023.
 

docs/yolov5/tutorials/transfer_learning_with_frozen_layers.md

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 📚 This guide explains how to **freeze** YOLOv5 🚀 layers when **transfer learning**. Transfer learning is a useful way to quickly retrain a model on new data without having to retrain the entire network. Instead, part of the initial weights are frozen in place, and the rest of the weights are used to compute loss and are updated by the optimizer. This requires less resources than normal training and allows for faster training times, though it may also result in reductions to final trained accuracy.  
 UPDATED 25 September 2022.