Use `conda install -c conda-forge ultralytics` (#4272)

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docs/help/contributing.md

@@ -6,7 +6,7 @@ keywords: Ultralytics, YOLO, open-source, contribute, pull request, bug report,
 
 # Contributing to Ultralytics Open-Source YOLO Repositories
 
-First of all, thank you for your interest in contributing to Ultralytics open-source YOLO repositories! Your contributions will help improve the project and benefit the community. This document provides guidelines and best practices for contributing to Ultralytics YOLO repositories.
+First of all, thank you for your interest in contributing to Ultralytics open-source YOLO repositories! Your contributions will help improve the project and benefit the community. This document provides guidelines and best practices to get you started.
 
 ## Table of Contents
 

docs/modes/predict.md

@@ -55,20 +55,21 @@ YOLOv8 can process different types of input sources for inference, as shown in t
     Use `stream=True` for processing long videos or large datasets to efficiently manage memory. When `stream=False`, the results for all frames or data points are stored in memory, which can quickly add up and cause out-of-memory errors for large inputs. In contrast, `stream=True` utilizes a generator, which only keeps the results of the current frame or data point in memory, significantly reducing memory consumption and preventing out-of-memory issues.
 
 | Source        | Argument                                   | Type            | Notes                                                                                       |
-|-------------|--------------------------------------------|---------------------------------------|----------------------------------------------------------------------------|
+|---------------|--------------------------------------------|-----------------|---------------------------------------------------------------------------------------------|
 | image         | `'image.jpg'`                              | `str` or `Path` | Single image file.                                                                          |
 | URL           | `'https://ultralytics.com/images/bus.jpg'` | `str`           | URL to an image.                                                                            |
 | screenshot    | `'screen'`                                 | `str`           | Capture a screenshot.                                                                       |
 | PIL           | `Image.open('im.jpg')`                     | `PIL.Image`     | HWC format with RGB channels.                                                               |
-| OpenCV      | `cv2.imread('im.jpg')`                     | `np.ndarray` of `uint8 (0-255)`       | HWC format with BGR channels.                                              |
+| OpenCV        | `cv2.imread('im.jpg')`                     | `np.ndarray`    | HWC format with BGR channels `uint8 (0-255)`.                                               |
-| numpy       | `np.zeros((640,1280,3))`                   | `np.ndarray` of `uint8 (0-255)`       | HWC format with BGR channels.                                              |
+| numpy         | `np.zeros((640,1280,3))`                   | `np.ndarray`    | HWC format with BGR channels `uint8 (0-255)`.                                               |
-| torch       | `torch.zeros(16,3,320,640)`                | `torch.Tensor` of `float32 (0.0-1.0)` | BCHW format with RGB channels.                                             |
+| torch         | `torch.zeros(16,3,320,640)`                | `torch.Tensor`  | BCHW format with RGB channels `float32 (0.0-1.0)`.                                          |
 | CSV           | `'sources.csv'`                            | `str` or `Path` | CSV file containing paths to images, videos, or directories.                                |
 | video ✅       | `'video.mp4'`                              | `str` or `Path` | Video file in formats like MP4, AVI, etc.                                                   |
 | directory ✅   | `'path/'`                                  | `str` or `Path` | Path to a directory containing images or videos.                                            |
 | glob ✅        | `'path/*.jpg'`                             | `str`           | Glob pattern to match multiple files. Use the `*` character as a wildcard.                  |
 | YouTube ✅     | `'https://youtu.be/Zgi9g1ksQHc'`           | `str`           | URL to a YouTube video.                                                                     |
 | stream ✅      | `'rtsp://example.com/media.mp4'`           | `str`           | URL for streaming protocols such as RTSP, RTMP, or an IP address.                           |
+| multi-stream ✅ | `'list.streams'`                           | `str` or `Path` | `*.streams` text file with one stream URL per row, i.e. 8 streams will run at batch-size 8. |
 
 Below are code examples for using each source type:
 
@@ -262,16 +263,19 @@ Below are code examples for using each source type:
         results = model(source, stream=True)  # generator of Results objects
         ```
 
-    === "Stream"
+    === "Streams"
-        Run inference on remote streaming sources using RTSP, RTMP, and IP address protocols.
+        Run inference on remote streaming sources using RTSP, RTMP, and IP address protocols. If mutliple streams are provided in a `*.streams` text file then batched inference will run, i.e. 8 streams will run at batch-size 8, otherwise single streams will run at batch-size 1.
         ```python
         from ultralytics import YOLO
 
         # Load a pretrained YOLOv8n model
         model = YOLO('yolov8n.pt')
 
-        # Define source as RTSP, RTMP or IP streaming address
+        # Single stream with batch-size 1 inference
-        source = 'rtsp://example.com/media.mp4'
+        source = 'rtsp://example.com/media.mp4'  # RTSP, RTMP or IP streaming address
+
+        # Multiple streams with batched inference (i.e. batch-size 8 for 8 streams)
+        source = 'path/to/list.streams'  # *.streams text file with one streaming address per row
 
         # Run inference on the source
         results = model(source, stream=True)  # generator of Results objects

docs/quickstart.md

@@ -28,7 +28,7 @@ Ultralytics provides various installation methods including pip, conda, and Dock
 
         ```bash
         # Install the ultralytics package using conda
-        conda install ultralytics
+        conda install -c conda-forge ultralytics
         ```
 
     === "Git clone"