ultralytics 8.0.122 Fix torch.Tensor inference (#3363)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: krzysztof.gonia <4281421+kgonia@users.noreply.github.com>

docs/quickstart.md

@@ -4,27 +4,65 @@ description: Install and use YOLOv8 via CLI or Python. Run single-line commands
 keywords: YOLOv8, object detection, segmentation, classification, pip, git, CLI, Python
 ---
 
-## Install
+## Install Ultralytics
 
-Install YOLOv8 via the `ultralytics` pip package for the latest stable release or by cloning
-the [https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics) repository for the most
-up-to-date version.
+Ultralytics provides various installation methods including pip, conda, and Docker. Install YOLOv8 via the `ultralytics` pip package for the latest stable release or by cloning the [Ultralytics GitHub repository](https://github.com/ultralytics/ultralytics) for the most up-to-date version. Docker can be used to execute the package in an isolated container, avoiding local installation.
 
 !!! example "Install"
 
-    === "pip install (recommended)"
+    === "Pip install (recommended)"
+        Install the `ultralytics` package using pip, or update an existing installation by running `pip install -U ultralytics`. Visit the Python Package Index (PyPI) for more details on the `ultralytics` package: [https://pypi.org/project/ultralytics/](https://pypi.org/project/ultralytics/).
         ```bash
+        # Install the ultralytics package using pip
         pip install ultralytics
         ```
-
-    === "git clone (for development)"
+    
+    === "Conda install"
+        Conda is an alternative package manager to pip which may also be used for installation. Visit Anaconda for more details at [https://anaconda.org/conda-forge/ultralytics](https://anaconda.org/conda-forge/ultralytics). Ultralytics feedstock repository for updating the conda package is at [https://github.com/conda-forge/ultralytics-feedstock/](https://github.com/conda-forge/ultralytics-feedstock/).
         ```bash
+        # Install the ultralytics package using conda
+        conda install ultralytics
+        ```
+    
+    === "Git clone"
+        Clone the `ultralytics` repository if you are interested in contributing to the development or wish to experiment with the latest source code. After cloning, navigate into the directory and install the package in editable mode `-e` using pip.
+        ```bash
+        # Clone the ultralytics repository
         git clone https://github.com/ultralytics/ultralytics
+        
+        # Navigate to the cloned directory
         cd ultralytics
+        
+        # Install the package in editable mode for development
         pip install -e .
         ```
 
-See the `ultralytics` [requirements.txt](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) file for a list of dependencies. Note that `pip` automatically installs all required dependencies.
+    === "Docker"
+        Utilize Docker to execute the `ultralytics` package in an isolated container. By employing the official `ultralytics` image from [Docker Hub](https://hub.docker.com/r/ultralytics/ultralytics), you can avoid local installation. Below are the commands to get the latest image and execute it:
+    
+        ```bash
+        # Set image name as a variable
+        t=ultralytics/ultralytics:latest
+        
+        # Pull the latest ultralytics image from Docker Hub
+        sudo docker pull $t
+        
+        # Run the ultralytics image in a container with GPU support
+        sudo docker run -it --ipc=host --gpus all $t
+        ```
+    
+        The above command initializes a Docker container with the latest `ultralytics` image. The `-it` flag assigns a pseudo-TTY and maintains stdin open, enabling you to interact with the container. The `--ipc=host` flag sets the IPC (Inter-Process Communication) namespace to the host, which is essential for sharing memory between processes. The `--gpus all` flag enables access to all available GPUs inside the container, which is crucial for tasks that require GPU computation.
+    
+        Note: To work with files on your local machine within the container, use Docker volumes for mounting a local directory into the container:
+    
+        ```bash
+        # Mount local directory to a directory inside the container
+        sudo docker run -it --ipc=host --gpus all -v /path/on/host:/path/in/container $t
+        ```
+    
+        Alter `/path/on/host` with the directory path on your local machine, and `/path/in/container` with the desired path inside the Docker container for accessibility.
+
+See the `ultralytics` [requirements.txt](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) file for a list of dependencies. Note that all examples above install all required dependencies.
 
 !!! tip "Tip"
 
@@ -34,9 +72,9 @@ See the `ultralytics` [requirements.txt](https://github.com/ultralytics/ultralyt
         <img width="800" alt="PyTorch Installation Instructions" src="https://user-images.githubusercontent.com/26833433/228650108-ab0ec98a-b328-4f40-a40d-95355e8a84e3.png">
     </a>
 
-## Use with CLI
+## Use Ultralytics with CLI
 
-The YOLO command line interface (CLI) allows for simple single-line commands without the need for a Python environment.
+The Ultralytics command line interface (CLI) allows for simple single-line commands without the need for a Python environment.
 CLI requires no customization or Python code. You can simply run all tasks from the terminal with the `yolo` command. Check out the [CLI Guide](usage/cli.md) to learn more about using YOLOv8 from the command line.
 
 !!! example
@@ -103,7 +141,7 @@ CLI requires no customization or Python code. You can simply run all tasks from
 
 [CLI Guide](usage/cli.md){ .md-button .md-button--primary}
 
-## Use with Python
+## Use Ultralytics with Python
 
 YOLOv8's Python interface allows for seamless integration into your Python projects, making it easy to load, run, and process the model's output. Designed with simplicity and ease of use in mind, the Python interface enables users to quickly implement object detection, segmentation, and classification in their projects. This makes YOLOv8's Python interface an invaluable tool for anyone looking to incorporate these functionalities into their Python projects.