diff --git a/docs/datasets/track/index.md b/docs/datasets/track/index.md index c25119e43f..7b31852c43 100644 --- a/docs/datasets/track/index.md +++ b/docs/datasets/track/index.md @@ -21,10 +21,10 @@ Support for training trackers alone is coming soon from ultralytics import YOLO model = YOLO('yolov8n.pt') - results = model.track(source="https://youtu.be/Zgi9g1ksQHc", conf=0.3, iou=0.5, show=True) + results = model.track(source="https://youtu.be/LNwODJXcvt4", conf=0.3, iou=0.5, show=True) ``` === "CLI" ```bash - yolo track model=yolov8n.pt source="https://youtu.be/Zgi9g1ksQHc" conf=0.3, iou=0.5 show + yolo track model=yolov8n.pt source="https://youtu.be/LNwODJXcvt4" conf=0.3, iou=0.5 show ``` diff --git a/docs/modes/predict.md b/docs/modes/predict.md index 439b0246ca..6abea26300 100644 --- a/docs/modes/predict.md +++ b/docs/modes/predict.md @@ -67,7 +67,7 @@ YOLOv8 can process different types of input sources for inference, as shown in t | 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. | +| YouTube ✅ | `'https://youtu.be/LNwODJXcvt4'` | `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. | @@ -257,7 +257,7 @@ Below are code examples for using each source type: model = YOLO('yolov8n.pt') # Define source as YouTube video URL - source = 'https://youtu.be/Zgi9g1ksQHc' + source = 'https://youtu.be/LNwODJXcvt4' # Run inference on the source results = model(source, stream=True) # generator of Results objects diff --git a/docs/modes/track.md b/docs/modes/track.md index a8fa2129dc..9192651373 100644 --- a/docs/modes/track.md +++ b/docs/modes/track.md @@ -37,18 +37,18 @@ To run the tracker on video streams, use a trained Detect, Segment or Pose model model = YOLO('path/to/best.pt') # Load a custom trained model # Perform tracking with the model - results = model.track(source="https://youtu.be/Zgi9g1ksQHc", show=True) # Tracking with default tracker - results = model.track(source="https://youtu.be/Zgi9g1ksQHc", show=True, tracker="bytetrack.yaml") # Tracking with ByteTrack tracker + results = model.track(source="https://youtu.be/LNwODJXcvt4", show=True) # Tracking with default tracker + results = model.track(source="https://youtu.be/LNwODJXcvt4", show=True, tracker="bytetrack.yaml") # Tracking with ByteTrack tracker ``` === "CLI" ```bash # Perform tracking with various models using the command line interface - yolo track model=yolov8n.pt source="https://youtu.be/Zgi9g1ksQHc" # Official Detect model - yolo track model=yolov8n-seg.pt source="https://youtu.be/Zgi9g1ksQHc" # Official Segment model - yolo track model=yolov8n-pose.pt source="https://youtu.be/Zgi9g1ksQHc" # Official Pose model - yolo track model=path/to/best.pt source="https://youtu.be/Zgi9g1ksQHc" # Custom trained model + yolo track model=yolov8n.pt source="https://youtu.be/LNwODJXcvt4" # Official Detect model + yolo track model=yolov8n-seg.pt source="https://youtu.be/LNwODJXcvt4" # Official Segment model + yolo track model=yolov8n-pose.pt source="https://youtu.be/LNwODJXcvt4" # Official Pose model + yolo track model=path/to/best.pt source="https://youtu.be/LNwODJXcvt4" # Custom trained model # Track using ByteTrack tracker yolo track model=path/to/best.pt tracker="bytetrack.yaml" @@ -71,14 +71,14 @@ Tracking configuration shares properties with Predict mode, such as `conf`, `iou # Configure the tracking parameters and run the tracker model = YOLO('yolov8n.pt') - results = model.track(source="https://youtu.be/Zgi9g1ksQHc", conf=0.3, iou=0.5, show=True) + results = model.track(source="https://youtu.be/LNwODJXcvt4", conf=0.3, iou=0.5, show=True) ``` === "CLI" ```bash # Configure tracking parameters and run the tracker using the command line interface - yolo track model=yolov8n.pt source="https://youtu.be/Zgi9g1ksQHc" conf=0.3, iou=0.5 show + yolo track model=yolov8n.pt source="https://youtu.be/LNwODJXcvt4" conf=0.3, iou=0.5 show ``` ### Tracker Selection @@ -94,14 +94,14 @@ Ultralytics also allows you to use a modified tracker configuration file. To do # Load the model and run the tracker with a custom configuration file model = YOLO('yolov8n.pt') - results = model.track(source="https://youtu.be/Zgi9g1ksQHc", tracker='custom_tracker.yaml') + results = model.track(source="https://youtu.be/LNwODJXcvt4", tracker='custom_tracker.yaml') ``` === "CLI" ```bash # Load the model and run the tracker with a custom configuration file using the command line interface - yolo track model=yolov8n.pt source="https://youtu.be/Zgi9g1ksQHc" tracker='custom_tracker.yaml' + yolo track model=yolov8n.pt source="https://youtu.be/LNwODJXcvt4" tracker='custom_tracker.yaml' ``` For a comprehensive list of tracking arguments, refer to the [ultralytics/cfg/trackers](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/trackers) page. diff --git a/docs/quickstart.md b/docs/quickstart.md index 5441f1a6b5..e70df60490 100644 --- a/docs/quickstart.md +++ b/docs/quickstart.md @@ -153,7 +153,7 @@ The Ultralytics command line interface (CLI) allows for simple single-line comma Predict a YouTube video using a pretrained segmentation model at image size 320: ```bash - yolo predict model=yolov8n-seg.pt source='https://youtu.be/Zgi9g1ksQHc' imgsz=320 + yolo predict model=yolov8n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320 ``` === "Val" diff --git a/docs/usage/cli.md b/docs/usage/cli.md index e08ef10a3e..ac2832cb20 100644 --- a/docs/usage/cli.md +++ b/docs/usage/cli.md @@ -34,7 +34,7 @@ CLI requires no customization or Python code. You can simply run all tasks from Predict a YouTube video using a pretrained segmentation model at image size 320: ```bash - yolo predict model=yolov8n-seg.pt source='https://youtu.be/Zgi9g1ksQHc' imgsz=320 + yolo predict model=yolov8n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320 ``` === "Val" @@ -196,7 +196,7 @@ Default arguments can be overridden by simply passing them as arguments in the C === "Predict" Predict a YouTube video using a pretrained segmentation model at image size 320: ```bash - yolo segment predict model=yolov8n-seg.pt source='https://youtu.be/Zgi9g1ksQHc' imgsz=320 + yolo segment predict model=yolov8n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320 ``` === "Val" diff --git a/docs/usage/python.md b/docs/usage/python.md index 1522c69e5d..9f301cf591 100644 --- a/docs/usage/python.md +++ b/docs/usage/python.md @@ -220,8 +220,8 @@ for applications such as surveillance systems or self-driving cars. model = YOLO('path/to/best.pt') # load a custom model # Track with the model - results = model.track(source="https://youtu.be/Zgi9g1ksQHc", show=True) - results = model.track(source="https://youtu.be/Zgi9g1ksQHc", show=True, tracker="bytetrack.yaml") + results = model.track(source="https://youtu.be/LNwODJXcvt4", show=True) + results = model.track(source="https://youtu.be/LNwODJXcvt4", show=True, tracker="bytetrack.yaml") ``` [Track Examples](../modes/track.md){ .md-button .md-button--primary} diff --git a/docs/yolov5/quickstart_tutorial.md b/docs/yolov5/quickstart_tutorial.md index d42d93d4f7..b943dc8550 100644 --- a/docs/yolov5/quickstart_tutorial.md +++ b/docs/yolov5/quickstart_tutorial.md @@ -55,7 +55,7 @@ python detect.py --weights yolov5s.pt --source 0 # list.txt # list of images list.streams # list of streams 'path/*.jpg' # glob - 'https://youtu.be/Zgi9g1ksQHc' # YouTube + 'https://youtu.be/LNwODJXcvt4' # YouTube 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream ``` diff --git a/ultralytics/cfg/__init__.py b/ultralytics/cfg/__init__.py index fadbb372fa..cc37ee560c 100644 --- a/ultralytics/cfg/__init__.py +++ b/ultralytics/cfg/__init__.py @@ -42,7 +42,7 @@ CLI_HELP_MSG = \ yolo train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01 2. Predict a YouTube video using a pretrained segmentation model at image size 320: - yolo predict model=yolov8n-seg.pt source='https://youtu.be/Zgi9g1ksQHc' imgsz=320 + yolo predict model=yolov8n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320 3. Val a pretrained detection model at batch-size 1 and image size 640: yolo val model=yolov8n.pt data=coco128.yaml batch=1 imgsz=640 diff --git a/ultralytics/data/loaders.py b/ultralytics/data/loaders.py index 6656596490..8aec7fad9c 100644 --- a/ultralytics/data/loaders.py +++ b/ultralytics/data/loaders.py @@ -48,7 +48,7 @@ class LoadStreams: # Start thread to read frames from video stream st = f'{i + 1}/{n}: {s}... ' if urlparse(s).hostname in ('www.youtube.com', 'youtube.com', 'youtu.be'): # if source is YouTube video - # YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/Zgi9g1ksQHc' + # YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/LNwODJXcvt4' s = get_best_youtube_url(s) s = eval(s) if s.isnumeric() else s # i.e. s = '0' local webcam if s == 0 and (is_colab() or is_kaggle()): diff --git a/ultralytics/engine/predictor.py b/ultralytics/engine/predictor.py index c64909041f..9da29ee8f6 100644 --- a/ultralytics/engine/predictor.py +++ b/ultralytics/engine/predictor.py @@ -11,7 +11,7 @@ Usage - sources: list.txt # list of images list.streams # list of streams 'path/*.jpg' # glob - 'https://youtu.be/Zgi9g1ksQHc' # YouTube + 'https://youtu.be/LNwODJXcvt4' # YouTube 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream Usage - formats: diff --git a/ultralytics/hub/session.py b/ultralytics/hub/session.py index 396f9b37bf..660d2c66df 100644 --- a/ultralytics/hub/session.py +++ b/ultralytics/hub/session.py @@ -117,7 +117,8 @@ class HUBTrainingSession: if data['status'] == 'new': # new model to start training self.train_args = { - 'batch': data['batch'], + # TODO deprecate before 8.1.0 + 'batch': data['batch' if 'batch' in data else 'batch_size'], 'epochs': data['epochs'], 'imgsz': data['imgsz'], 'patience': data['patience'], diff --git a/ultralytics/utils/__init__.py b/ultralytics/utils/__init__.py index bdf981a180..c8a9110980 100644 --- a/ultralytics/utils/__init__.py +++ b/ultralytics/utils/__init__.py @@ -77,7 +77,7 @@ HELP_MSG = \ yolo detect train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01 - Predict a YouTube video using a pretrained segmentation model at image size 320: - yolo segment predict model=yolov8n-seg.pt source='https://youtu.be/Zgi9g1ksQHc' imgsz=320 + yolo segment predict model=yolov8n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320 - Val a pretrained detection model at batch-size 1 and image size 640: yolo detect val model=yolov8n.pt data=coco128.yaml batch=1 imgsz=640