ultralytics 8.0.196 instance-mean Segment loss (#5285)

Co-authored-by: Andy <39454881+yermandy@users.noreply.github.com>

docs/quickstart.md

@@ -20,8 +20,8 @@ Ultralytics provides various installation methods including pip, conda, and Dock
         pip install ultralytics
         ```
 
-        You can also install the `ultralytics` package directly from the GitHub [repository](https://github.com/ultralytics/ultralytics). This might be useful if you want the latest development version. Make sure to have the Git command-line tool installed on your system. The `@main` command installs the `main` branch and may be modified to another branch, i.e. `@my-branch`, or removed alltogether to default to `main` branch.
-    
+        You can also install the `ultralytics` package directly from the GitHub [repository](https://github.com/ultralytics/ultralytics). This might be useful if you want the latest development version. Make sure to have the Git command-line tool installed on your system. The `@main` command installs the `main` branch and may be modified to another branch, i.e. `@my-branch`, or removed entirely to default to `main` branch.
+
         ```bash
         # Install the ultralytics package from GitHub
         pip install git+https://github.com/ultralytics/ultralytics.git@main
@@ -40,15 +40,15 @@ Ultralytics provides various installation methods including pip, conda, and Dock
         ```
 
         !!! note
-        
+
             If you are installing in a CUDA environment best practice is to install `ultralytics`, `pytorch` and `pytorch-cuda` in the same command to allow the conda package manager to resolve any conflicts, or else to install `pytorch-cuda` last to allow it override the CPU-specific `pytorch` package if necessary.
             ```bash
             # Install all packages together using conda
-            conda install -c pytorch -c nvidia -c conda-forge pytorch torchvision pytorch-cuda=11.8 ultralytics 
+            conda install -c pytorch -c nvidia -c conda-forge pytorch torchvision pytorch-cuda=11.8 ultralytics
             ```
 
         ### Conda Docker Image
-    
+
         Ultralytics Conda Docker images are also available from [DockerHub](https://hub.docker.com/r/ultralytics/ultralytics). These images are based on [Miniconda3](https://docs.conda.io/projects/miniconda/en/latest/) and are an simple way to start using `ultralytics` in a Conda environment.
 
         ```bash
@@ -79,7 +79,7 @@ Ultralytics provides various installation methods including pip, conda, and Dock
     === "Docker"
 
         Utilize Docker to effortlessly execute the `ultralytics` package in an isolated container, ensuring consistent and smooth performance across various environments. By choosing one of the official `ultralytics` images from [Docker Hub](https://hub.docker.com/r/ultralytics/ultralytics), you not only avoid the complexity of local installation but also benefit from access to a verified working environment. Ultralytics offers 5 main supported Docker images, each designed to provide high compatibility and efficiency for different platforms and use cases:
-        
+
         <a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>
 
         - **Dockerfile:** GPU image recommended for training.
@@ -88,7 +88,7 @@ Ultralytics provides various installation methods including pip, conda, and Dock
         - **Dockerfile-jetson:** Tailored for NVIDIA Jetson devices, integrating GPU support optimized for these platforms.
         - **Dockerfile-python:** Minimal image with just Python and necessary dependencies, ideal for lightweight applications and development.
         - **Dockerfile-conda:** Based on Miniconda3 with conda installation of ultralytics package.
-        
+
         Below are the commands to get the latest image and execute it:
 
         ```bash