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ultralytics 8.2.2 replace COCO128 with COCO8 (#10167)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Glenn Jocher 2024-04-18 20:47:21 -07:00 • committed by GitHub
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4
docs/en/guides/azureml-quickstart.md
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@ -74,7 +74,7 @@ yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
Train a detection model for 10 epochs with an initial learning_rate of 0.01:
```bash
yolo train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01
yolo train data=coco8.yaml model=yolov8n.pt epochs=10 lr0=0.01
```
You can find more [instructions to use the Ultralytics CLI here](../quickstart.md#use-ultralytics-with-cli).
@ -131,7 +131,7 @@ from ultralytics import YOLO
model = YOLO("yolov8n.pt") # load an official YOLOv8n model
# Use the model
model.train(data="coco128.yaml", epochs=3) # train the model
model.train(data="coco8.yaml", epochs=3) # train the model
metrics = model.val() # evaluate model performance on the validation set
results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
path = model.export(format="onnx") # export the model to ONNX format

10
docs/en/guides/nvidia-jetson.md
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@ -205,17 +205,17 @@ To reproduce the above Ultralytics benchmarks on all export [formats](../modes/e
# Load a YOLOv8n PyTorch model
model = YOLO('yolov8n.pt')
# Benchmark YOLOv8n speed and accuracy on the COCO128 dataset for all all export formats
results = model.benchmarks(data='coco128.yaml', imgsz=640)
# Benchmark YOLOv8n speed and accuracy on the COCO8 dataset for all all export formats
results = model.benchmarks(data='coco8.yaml', imgsz=640)
```
=== "CLI"
```bash
# Benchmark YOLOv8n speed and accuracy on the COCO128 dataset for all all export formats
yolo benchmark model=yolov8n.pt data=coco128.yaml imgsz=640
# Benchmark YOLOv8n speed and accuracy on the COCO8 dataset for all all export formats
yolo benchmark model=yolov8n.pt data=coco8.yaml imgsz=640
```
Note that benchmarking results might vary based on the exact hardware and software configuration of a system, as well as the current workload of the system at the time the benchmarks are run. For the most reliable results use a dataset with a large number of images, i.e. `data='coco128.yaml' (128 val images), or `data='coco.yaml'` (5000 val images).
Note that benchmarking results might vary based on the exact hardware and software configuration of a system, as well as the current workload of the system at the time the benchmarks are run. For the most reliable results use a dataset with a large number of images, i.e. `data='coco8.yaml' (128 val images), or `data='coco.yaml'` (5000 val images).
!!! Note

32
docs/en/guides/object-counting.md
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@ -219,22 +219,22 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
### Optional Arguments `set_args`
| Name | Type | Default | Description |
|-----------------------|-------------|----------------------------|--------------------------------------------------|
| `view_img` | `bool` | `False` | Display frames with counts |
| `view_in_counts` | `bool` | `True` | Display in-counts only on video frame |
| `view_out_counts` | `bool` | `True` | Display out-counts only on video frame |
| `line_thickness` | `int` | `2` | Increase bounding boxes and count text thickness |
| `reg_pts` | `list` | `[(20, 400), (1260, 400)]` | Points defining the Region Area |
| `classes_names` | `dict` | `model.model.names` | Dictionary of Class Names |
| `count_reg_color` | `RGB Color` | `(255, 0, 255)` | Color of the Object counting Region or Line |
| `track_thickness` | `int` | `2` | Thickness of Tracking Lines |
| `draw_tracks` | `bool` | `False` | Enable drawing Track lines |
| `track_color` | `RGB Color` | `(0, 255, 0)` | Color for each track line |
| `line_dist_thresh` | `int` | `15` | Euclidean Distance threshold for line counter |
| `count_txt_color` | `RGB Color` | `(255, 255, 255)` | Foreground color for Object counts text |
| `region_thickness` | `int` | `5` | Thickness for object counter region or line |
| `count_bg_color` | `RGB Color` | `(255, 255, 255)` | Count highlighter color |
| Name | Type | Default | Description |
|--------------------|-------------|----------------------------|--------------------------------------------------|
| `view_img` | `bool` | `False` | Display frames with counts |
| `view_in_counts` | `bool` | `True` | Display in-counts only on video frame |
| `view_out_counts` | `bool` | `True` | Display out-counts only on video frame |
| `line_thickness` | `int` | `2` | Increase bounding boxes and count text thickness |
| `reg_pts` | `list` | `[(20, 400), (1260, 400)]` | Points defining the Region Area |
| `classes_names` | `dict` | `model.model.names` | Dictionary of Class Names |
| `count_reg_color` | `RGB Color` | `(255, 0, 255)` | Color of the Object counting Region or Line |
| `track_thickness` | `int` | `2` | Thickness of Tracking Lines |
| `draw_tracks` | `bool` | `False` | Enable drawing Track lines |
| `track_color` | `RGB Color` | `(0, 255, 0)` | Color for each track line |
| `line_dist_thresh` | `int` | `15` | Euclidean Distance threshold for line counter |
| `count_txt_color` | `RGB Color` | `(255, 255, 255)` | Foreground color for Object counts text |
| `region_thickness` | `int` | `5` | Thickness for object counter region or line |
| `count_bg_color` | `RGB Color` | `(255, 255, 255)` | Count highlighter color |
### Arguments `model.track`

3
docs/en/guides/workouts-monitoring.md
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@ -19,7 +19,6 @@ Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://gi
<strong>Watch:</strong> Workouts Monitoring using Ultralytics YOLOv8 | Pushups, Pullups, Ab Workouts
</p>
## Advantages of Workouts Monitoring?
- **Optimized Performance:** Tailoring workouts based on monitoring data for better results.
@ -157,4 +156,4 @@ Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://gi
| `conf` | `float` | `0.3` | Confidence Threshold |
| `iou` | `float` | `0.5` | IOU Threshold |
| `classes` | `list` | `None` | filter results by class, i.e. classes=0, or classes=[0,2,3] |
| `verbose` | `bool` | `True` | Display the object tracking results |
| `verbose` | `bool` | `True` | Display the object tracking results |