ultralytics 8.0.82 docs updates and fixes (#2098)

Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: Aurelio Losquiño Muñoz <38859113+aurelm95@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Paula Derrenger <107626595+pderrenger@users.noreply.github.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>

docs/tasks/classify.md

@@ -69,6 +69,15 @@ see the [Configuration](../usage/cfg.md) page.
         yolo classify train data=mnist160 model=yolov8n-cls.yaml pretrained=yolov8n-cls.pt epochs=100 imgsz=64
         ```
 
+### Dataset format
+The YOLO classification dataset format is same as the torchvision format. Each class of images has its own folder and you have to simply pass the path of the dataset folder, i.e, `yolo classify train data="path/to/dataset"`
+```
+dataset/
+├── class1/
+├── class2/
+├── class3/
+├── ...
+```
 ## Val
 
 Validate trained YOLOv8n-cls model accuracy on the MNIST160 dataset. No argument need to passed as the `model` retains

docs/tasks/detect.md

@@ -67,6 +67,9 @@ the [Configuration](../usage/cfg.md) page.
         # Build a new model from YAML, transfer pretrained weights to it and start training
         yolo detect train data=coco128.yaml model=yolov8n.yaml pretrained=yolov8n.pt epochs=100 imgsz=640
         ```
+### Dataset format
+YOLO detection dataset format can be found in detail in the [Dataset Guide](../yolov5/train_custom_data.md).
+To convert your existing dataset from other formats( like COCO, VOC etc.) to YOLO format, please use [json2yolo tool](https://github.com/ultralytics/JSON2YOLO) by Ultralytics.
 
 ## Val
 

docs/tasks/segment.md

@@ -68,6 +68,13 @@ arguments see the [Configuration](../usage/cfg.md) page.
         yolo segment train data=coco128-seg.yaml model=yolov8n-seg.yaml pretrained=yolov8n-seg.pt epochs=100 imgsz=640
         ```
 
+### Dataset format
+YOLO segmentation dataset label format extends detection format with segment points.
+
+`cls x1 y1 x2 y2 p1 p2 ... pn`
+
+To convert your existing dataset from other formats( like COCO, VOC etc.) to YOLO format, please use [json2yolo tool](https://github.com/ultralytics/JSON2YOLO) by Ultralytics.
+
 ## Val
 
 Validate trained YOLOv8n-seg model accuracy on the COCO128-seg dataset. No argument need to passed as the `model`