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OBB Docs updates (#7568)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Muhammad Rizwan Munawar <chr043416@gmail.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-01-13 23:08:31 +01:00 • committed by GitHub
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18
docs/en/tasks/obb.md
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@ -42,7 +42,7 @@ YOLOv8 pretrained OBB models are shown here, which are pretrained on the [DOTAv1
## Train
Train YOLOv8n-obb on the dota8.yaml dataset for 100 epochs at image size 640. For a full list of available arguments see the [Configuration](../usage/cfg.md) page.
Train YOLOv8n-obb on the `dota8.yaml` dataset for 100 epochs at image size 640. For a full list of available arguments see the [Configuration](../usage/cfg.md) page.
!!! Example
@ -57,19 +57,19 @@ Train YOLOv8n-obb on the dota8.yaml dataset for 100 epochs at image size 640. Fo
model = YOLO('yolov8n-obb.yaml').load('yolov8n.pt') # build from YAML and transfer weights
# Train the model
results = model.train(data='dota8-obb.yaml', epochs=100, imgsz=640)
results = model.train(data='dota8.yaml', epochs=100, imgsz=640)
```
=== "CLI"
```bash
# Build a new model from YAML and start training from scratch
yolo obb train data=dota8-obb.yaml model=yolov8n-obb.yaml epochs=100 imgsz=640
yolo obb train data=dota8.yaml model=yolov8n-obb.yaml epochs=100 imgsz=640
# Start training from a pretrained *.pt model
yolo obb train data=dota8-obb.yaml model=yolov8n-obb.pt epochs=100 imgsz=640
yolo obb train data=dota8.yaml model=yolov8n-obb.pt epochs=100 imgsz=640
# Build a new model from YAML, transfer pretrained weights to it and start training
yolo obb train data=dota8-obb.yaml model=yolov8n-obb.yaml pretrained=yolov8n-obb.pt epochs=100 imgsz=640
yolo obb train data=dota8.yaml model=yolov8n-obb.yaml pretrained=yolov8n-obb.pt epochs=100 imgsz=640
```
### Dataset format
@ -78,7 +78,7 @@ OBB dataset format can be found in detail in the [Dataset Guide](../datasets/obb
## Val
Validate trained YOLOv8n-obb model accuracy on the dota8-obb dataset. No argument need to passed as the `model`
Validate trained YOLOv8n-obb model accuracy on the DOTA8 dataset. No argument need to passed as the `model`
retains it's training `data` and arguments as model attributes.
!!! Example
@ -93,7 +93,7 @@ retains it's training `data` and arguments as model attributes.
model = YOLO('path/to/best.pt') # load a custom model
# Validate the model
metrics = model.val() # no arguments needed, dataset and settings remembered
metrics = model.val(data='dota8.yaml') # no arguments needed, dataset and settings remembered
metrics.box.map # map50-95(B)
metrics.box.map50 # map50(B)
metrics.box.map75 # map75(B)
@ -102,8 +102,8 @@ retains it's training `data` and arguments as model attributes.
=== "CLI"
```bash
yolo obb val model=yolov8n-obb.pt # val official model
yolo obb val model=path/to/best.pt # val custom model
yolo obb val model=yolov8n-obb.pt data=dota8.yaml # val official model
yolo obb val model=path/to/best.pt data=path/to/data.yaml # val custom model
```
## Predict