Add dota8.yaml and O tests (#7394)

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

ultralytics/cfg/datasets/DOTAv1.5.yaml

@@ -5,7 +5,7 @@
 # parent
 # ├── ultralytics
 # └── datasets
-#     └── dota2  ← downloads here (2GB)
+#     └── dota1.5  ← downloads here (2GB)
 
 # 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, ..]
 path: ../datasets/DOTAv1.5  # dataset root dir

ultralytics/cfg/datasets/DOTAv1.yaml

@@ -5,7 +5,7 @@
 # parent
 # ├── ultralytics
 # └── datasets
-#     └── dota2  ← downloads here (2GB)
+#     └── dota1  ← downloads here (2GB)
 
 # 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, ..]
 path: ../datasets/DOTAv1  # dataset root dir

ultralytics/cfg/datasets/dota8.yaml

@@ -0,0 +1,34 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# DOTA8 dataset 8 images from split DOTAv1 dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/obb/dota8/
+# Example usage: yolo train model=yolov8n-obb.pt data=dota8.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── dota8  ← downloads here (1MB)
+
+# 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, ..]
+path: ../datasets/dota8  # dataset root dir
+train: images/train  # train images (relative to 'path') 4 images
+val: images/val  # val images (relative to 'path') 4 images
+
+# Classes for DOTA 1.0
+names:
+  0: plane
+  1: ship
+  2: storage tank
+  3: baseball diamond
+  4: tennis court
+  5: basketball court
+  6: ground track field
+  7: harbor
+  8: bridge
+  9: large vehicle
+  10: small vehicle
+  11: helicopter
+  12: roundabout
+  13: soccer ball field
+  14: swimming pool
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/yolov5/releases/download/v1.0/dota8.zip

ultralytics/engine/results.py

@@ -323,6 +323,9 @@ class Results(SimpleClass):
         if self.probs is not None:
             LOGGER.warning('WARNING ⚠️ Classify task do not support `save_crop`.')
             return
+        if self.obb is not None:
+            LOGGER.warning('WARNING ⚠️ OBB task do not support `save_crop`.')
+            return
         for d in self.boxes:
             save_one_box(d.xyxy,
                          self.orig_img.copy(),

ultralytics/models/yolo/obb/val.py

@@ -106,6 +106,17 @@ class OBBValidator(DetectionValidator):
                 'rbox': [round(x, 3) for x in r],
                 'poly': [round(x, 3) for x in b]})
 
+    def save_one_txt(self, predn, save_conf, shape, file):
+        """Save YOLO detections to a txt file in normalized coordinates in a specific format."""
+        gn = torch.tensor(shape)[[1, 0, 1, 0]]  # normalization gain whwh
+        for *xyxy, conf, cls, angle in predn.tolist():
+            xywha = torch.tensor([*xyxy, angle]).view(1, 5)
+            xywha[:, :4] /= gn
+            xyxyxyxy = ops.xywhr2xyxyxyxy(xywha).view(-1).tolist()  # normalized xywh
+            line = (cls, *xyxyxyxy, conf) if save_conf else (cls, *xyxyxyxy)  # label format
+            with open(file, 'a') as f:
+                f.write(('%g ' * len(line)).rstrip() % line + '\n')
+
     def eval_json(self, stats):
         """Evaluates YOLO output in JSON format and returns performance statistics."""
         if self.args.save_json and self.is_dota and len(self.jdict):

ultralytics/models/yolo/segment/train.py

@@ -51,7 +51,7 @@ class SegmentationTrainer(yolo.detect.DetectionTrainer):
                     batch['batch_idx'],
                     batch['cls'].squeeze(-1),
                     batch['bboxes'],
-                    batch['masks'],
+                    masks=batch['masks'],
                     paths=batch['im_file'],
                     fname=self.save_dir / f'train_batch{ni}.jpg',
                     on_plot=self.on_plot)