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Fix inaccuracies in OBB docs (#16253)

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Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Mohammed Yasin 2024-09-13 17:34:23 +08:00 • committed by GitHub
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docs/en/datasets/obb/dota-v2.md
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@ -111,15 +111,15 @@ To train a model on the DOTA v1 dataset, you can utilize the following code snip
# Create a new YOLOv8n-OBB model from scratch
model = YOLO("yolov8n-obb.yaml")
# Train the model on the DOTAv2 dataset
results = model.train(data="DOTAv1.yaml", epochs=100, imgsz=640)
# Train the model on the DOTAv1 dataset
results = model.train(data="DOTAv1.yaml", epochs=100, imgsz=1024)
```
=== "CLI"
```bash
# Train a new YOLOv8n-OBB model on the DOTAv2 dataset
yolo obb train data=DOTAv1.yaml model=yolov8n-obb.pt epochs=100 imgsz=640
# Train a new YOLOv8n-OBB model on the DOTAv1 dataset
yolo obb train data=DOTAv1.yaml model=yolov8n-obb.pt epochs=100 imgsz=1024
```
## Sample Data and Annotations
@ -180,14 +180,14 @@ To train a model on the DOTA dataset, you can use the following example with Ult
model = YOLO("yolov8n-obb.yaml")
# Train the model on the DOTAv1 dataset
results = model.train(data="DOTAv1.yaml", epochs=100, imgsz=640)
results = model.train(data="DOTAv1.yaml", epochs=100, imgsz=1024)
```
=== "CLI"
```bash
# Train a new YOLOv8n-OBB model on the DOTAv1 dataset
yolo obb train data=DOTAv1.yaml model=yolov8n-obb.pt epochs=100 imgsz=640
yolo obb train data=DOTAv1.yaml model=yolov8n-obb.pt epochs=100 imgsz=1024
```
For more details on how to split and preprocess the DOTA images, refer to the [split DOTA images section](#split-dota-images).