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Update YOLOv3 and YOLOv5 YAMLs (#7574)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Glenn Jocher 2024-01-14 20:10:32 +01:00 • committed by GitHub
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docs/en/datasets/detect/argoverse.md
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@ -10,7 +10,7 @@ The [Argoverse](https://www.argoverse.org/) dataset is a collection of data desi
!!! Note
The Argoverse dataset *.zip file required for training was removed from Amazon S3 after the shutdown of Argo AI by Ford, but we have made it available for manual download on [Google Drive](https://drive.google.com/file/d/1st9qW3BeIwQsnR0t8mRpvbsSWIo16ACi/view?usp=drive_link).
The Argoverse dataset `*.zip` file required for training was removed from Amazon S3 after the shutdown of Argo AI by Ford, but we have made it available for manual download on [Google Drive](https://drive.google.com/file/d/1st9qW3BeIwQsnR0t8mRpvbsSWIo16ACi/view?usp=drive_link).
## Key Features

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docs/en/datasets/detect/index.md
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@ -12,7 +12,7 @@ Training a robust and accurate object detection model requires a comprehensive d
### Ultralytics YOLO format
The Ultralytics YOLO format is a dataset configuration format that allows you to define the dataset root directory, the relative paths to training/validation/testing image directories or *.txt files containing image paths, and a dictionary of class names. Here is an example:
The Ultralytics YOLO format is a dataset configuration format that allows you to define the dataset root directory, the relative paths to training/validation/testing image directories or `*.txt` files containing image paths, and a dictionary of class names. Here is an example:
```yaml
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]

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docs/en/datasets/detect/voc.md
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@ -58,8 +58,7 @@ To train a YOLOv8n model on the VOC dataset for 100 epochs with an image size of
=== "CLI"
```bash
# Start training from
a pretrained *.pt model
# Start training from a pretrained *.pt model
yolo detect train data=VOC.yaml model=yolov8n.pt epochs=100 imgsz=640
```