Chiebot-Mirror
/
ultralytics
mirror of https://github.com/ultralytics/ultralytics.git
9
0
Fork 1
Code Issues Packages Projects Releases Wiki Activity

Update YouTube URL https://youtu.be/LNwODJXcvt4 (#4808)

Browse Source
pull/4813/head
Glenn Jocher 1 year ago committed by GitHub
parent 16ce193d6e
commit dbc53f6741
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
12 changed files with 26 additions and 25 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 4
      docs/datasets/track/index.md
  2. 4
      docs/modes/predict.md
  3. 20
      docs/modes/track.md
  4. 2
      docs/quickstart.md
  5. 4
      docs/usage/cli.md
  6. 4
      docs/usage/python.md
  7. 2
      docs/yolov5/quickstart_tutorial.md
  8. 2
      ultralytics/cfg/__init__.py
  9. 2
      ultralytics/data/loaders.py
  10. 2
      ultralytics/engine/predictor.py
  11. 3
      ultralytics/hub/session.py
  12. 2
      ultralytics/utils/__init__.py

4
docs/datasets/track/index.md
Unescape View File

@ -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
```

4
docs/modes/predict.md
Unescape View File

@ -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

20
docs/modes/track.md
Unescape View File

@ -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.

2
docs/quickstart.md
Unescape View File

@ -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"

4
docs/usage/cli.md
Unescape View File

@ -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"

4
docs/usage/python.md
Unescape View File

@ -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}

2
docs/yolov5/quickstart_tutorial.md
Unescape View File

@ -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
```

2
ultralytics/cfg/__init__.py
Unescape View File

@ -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

2
ultralytics/data/loaders.py
Unescape View File

@ -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()):

2
ultralytics/engine/predictor.py
Unescape View File

@ -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:

3
ultralytics/hub/session.py
Unescape View File

@ -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'],

2
ultralytics/utils/__init__.py
Unescape View File

@ -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

Write Preview
Loading…
Cancel
Save