Add dota8.yaml and O tests (ultralytics#7394)

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

Author: Laughing-q

README.md

@@ -199,7 +199,7 @@ See [OBB Docs](https://docs.ultralytics.com/tasks/obb/) for usage examples with
 | [YOLOv8l-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-obb.pt) | 1024                  | 80.7               | 1278.42                        | 11.83                               | 44.5               | 433.8             |
 | [YOLOv8x-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-obb.pt) | 1024                  | 81.36              | 1759.10                        | 13.23                               | 69.5               | 676.7             |
 
-- **mAP<sup>test</sup>** values are for single-model multi-scale on [DOTAv1](https://captain-whu.github.io/DOTA/index.html) dataset. <br>Reproduce by `yolo val obb data=DOTAv1.yaml device=0 split=test`
+- **mAP<sup>test</sup>** values are for single-model multi-scale on [DOTAv1](https://captain-whu.github.io/DOTA/index.html) dataset. <br>Reproduce by `yolo val obb data=DOTAv1.yaml device=0 split=test` and submit merged results to [DOTA evaluation](https://captain-whu.github.io/DOTA/evaluation.html).
 - **Speed** averaged over DOTAv1 val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu`
 
 </details>

docs/en/datasets/obb/dota-v2.md

@@ -66,13 +66,40 @@ Typically, datasets incorporate a YAML (Yet Another Markup Language) file detail
     --8<-- "ultralytics/cfg/datasets/DOTAv1.yaml"
     ```
 
+## Split DOTA images
+
+To train DOTA dataset, We split original DOTA images with high-resolution into images with 1024x1024 resolution in multi-scale way.
+
+!!! Example "Split images"
+
+    === "Python"
+
+        ```python
+        from ultralytics.data.split_dota import split_trainval, split_test
+
+        # split train and val set, with labels.
+        split_trainval(
+            data_root='path/to/DOTAv1.0/',
+            save_dir='path/to/DOTAv1.0-split/',
+            rates=[0.5, 1.0, 1.5],    # multi-scale
+            gap=500
+        )
+        # split test set, without labels.
+        split_test(
+            data_root='path/to/DOTAv1.0/',
+            save_dir='path/to/DOTAv1.0-split/',
+            rates=[0.5, 1.0, 1.5],    # multi-scale
+            gap=500
+        )
+        ```
+
 ## Usage
 
 To train a model on the DOTA v1 dataset, you can utilize the following code snippets. Always refer to your model's documentation for a thorough list of available arguments.
 
 !!! Warning
 
-    Please note that all images and associated annotations in the DOTAv2 dataset can be used for academic purposes, but commercial use is prohibited. Your understanding and respect for the dataset creators' wishes are greatly appreciated!
+    Please note that all images and associated annotations in the DOTAv1 dataset can be used for academic purposes, but commercial use is prohibited. Your understanding and respect for the dataset creators' wishes are greatly appreciated!
 
 !!! Example "Train Example"
 

docs/en/datasets/obb/dota8.md

@@ -0,0 +1,81 @@
+---
+comments: true
+description: Discover the versatile DOTA8 dataset, perfect for testing and debugging oriented detection models. Learn how to get started with YOLOv8-obb model training.
+keywords: Ultralytics, YOLOv8, oriented detection, DOTA8 dataset, dataset, model training, YAML
+---
+
+# DOTA8 Dataset
+
+## Introduction
+
+[Ultralytics](https://ultralytics.com) DOTA8 is a small, but versatile oriented object detection dataset composed of the first 8 images of 8 images of the split DOTAv1 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging object detection models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
+
+This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com) and [YOLOv8](https://github.com/ultralytics/ultralytics).
+
+## Dataset YAML
+
+A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the DOTA8 dataset, the `dota8.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/dota8.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/dota8.yaml).
+
+!!! Example "ultralytics/cfg/datasets/dota8.yaml"
+
+    ```yaml
+    --8<-- "ultralytics/cfg/datasets/dota8.yaml"
+    ```
+
+## Usage
+
+To train a YOLOv8n-obb model on the DOTA8 dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! Example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n-obb.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data='dota8.yaml', epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo obb train data=dota8.yaml model=yolov8n-obb.pt epochs=100 imgsz=640
+        ```
+
+## Sample Images and Annotations
+
+Here are some examples of images from the DOTA8 dataset, along with their corresponding annotations:
+
+<img src="https://github.com/Laughing-q/assets/assets/61612323/965d3ff7-5b9b-4add-b62e-9795921b60de" alt="Dataset sample image" width="800">
+
+- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
+
+The example showcases the variety and complexity of the images in the DOTA8 dataset and the benefits of using mosaicing during the training process.
+
+## Citations and Acknowledgments
+
+If you use the DOTA dataset in your research or development work, please cite the following paper:
+
+!!! Quote ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @article{9560031,
+          author={Ding, Jian and Xue, Nan and Xia, Gui-Song and Bai, Xiang and Yang, Wen and Yang, Michael and Belongie, Serge and Luo, Jiebo and Datcu, Mihai and Pelillo, Marcello and Zhang, Liangpei},
+          journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
+          title={Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges},
+          year={2021},
+          volume={},
+          number={},
+          pages={1-1},
+          doi={10.1109/TPAMI.2021.3117983}
+        }
+        ```
+
+A special note of gratitude to the team behind the DOTA datasets for their commendable effort in curating this dataset. For an exhaustive understanding of the dataset and its nuances, please visit the [official DOTA website](https://captain-whu.github.io/DOTA/index.html).

docs/en/tasks/obb.md

@@ -32,14 +32,12 @@ YOLOv8 pretrained OBB models are shown here, which are pretrained on the [DOTAv1
 | [YOLOv8l-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-obb.pt) | 1024                  | 80.7                 | 1278.42                          | 11.83                                 | 44.5                 | 433.8             |
 | [YOLOv8x-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-obb.pt) | 1024                  | 81.36                | 1759.10                          | 13.23                                 | 69.5                 | 676.7             |
 
-- **mAP<sup>test</sup>** values are for single-model multi-scale on [DOTAv1 test](http://cocodataset.org) dataset. <br>Reproduce by `yolo val obb data=DOTAv1.yaml device=0 split=test`
+- **mAP<sup>test</sup>** values are for single-model multi-scale on [DOTAv1 test](https://captain-whu.github.io/DOTA/index.html) dataset. <br>Reproduce by `yolo val obb data=DOTAv1.yaml device=0 split=test` and submit merged results to [DOTA evaluation](https://captain-whu.github.io/DOTA/evaluation.html).
 - **Speed** averaged over DOTAv1 val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu`
 
 ## Train
 
-<!-- TODO: probably we should create a sample dataset like coco128.yaml, named dota128.yaml? -->
-
-Train YOLOv8n-obb on the dota128.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
 
@@ -54,19 +52,19 @@ Train YOLOv8n-obb on the dota128.yaml dataset for 100 epochs at image size 640.
         model = YOLO('yolov8n-obb.yaml').load('yolov8n.pt')  # build from YAML and transfer weights
 
         # Train the model
-        results = model.train(data='dota128-obb.yaml', epochs=100, imgsz=640)
+        results = model.train(data='dota8-obb.yaml', epochs=100, imgsz=640)
         ```
     === "CLI"
 
         ```bash
         # Build a new model from YAML and start training from scratch
-        yolo obb train data=dota128-obb.yaml model=yolov8n-obb.yaml epochs=100 imgsz=640
+        yolo obb train data=dota8-obb.yaml model=yolov8n-obb.yaml epochs=100 imgsz=640
 
         # Start training from a pretrained *.pt model
-        yolo obb train data=dota128-obb.yaml model=yolov8n-obb.pt epochs=100 imgsz=640
+        yolo obb train data=dota8-obb.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=dota128-obb.yaml model=yolov8n-obb.yaml pretrained=yolov8n-obb.pt epochs=100 imgsz=640
+        yolo obb train data=dota8-obb.yaml model=yolov8n-obb.yaml pretrained=yolov8n-obb.pt epochs=100 imgsz=640
         ```
 
 ### Dataset format
@@ -75,7 +73,7 @@ OBB dataset format can be found in detail in the [Dataset Guide](../datasets/obb
 
 ## Val
 
-Validate trained YOLOv8n-obb model accuracy on the dota128-obb dataset. No argument need to passed as the `model`
+Validate trained YOLOv8n-obb model accuracy on the dota8-obb dataset. No argument need to passed as the `model`
 retains it's training `data` and arguments as model attributes.
 
 !!! Example

docs/mkdocs.yml

@@ -260,6 +260,7 @@ nav:
       - Oriented Bounding Boxes (OBB):
           - datasets/obb/index.md
           - DOTAv2: datasets/obb/dota-v2.md
+          - DOTA8: datasets/obb/dota8.md
       - Multi-Object Tracking:
           - datasets/track/index.md
   - Guides:

tests/test_cli.py

@@ -13,12 +13,14 @@
     ('detect', 'yolov8n', 'coco8.yaml'),
     ('segment', 'yolov8n-seg', 'coco8-seg.yaml'),
     ('classify', 'yolov8n-cls', 'imagenet10'),
-    ('pose', 'yolov8n-pose', 'coco8-pose.yaml'), ]  # (task, model, data)
+    ('pose', 'yolov8n-pose', 'coco8-pose.yaml'),
+    ('obb', 'yolov8n-obb', 'dota8.yaml'), ]  # (task, model, data)
 EXPORT_ARGS = [
     ('yolov8n', 'torchscript'),
     ('yolov8n-seg', 'torchscript'),
     ('yolov8n-cls', 'torchscript'),
-    ('yolov8n-pose', 'torchscript'), ]  # (model, format)
+    ('yolov8n-pose', 'torchscript'),
+    ('yolov8n-obb', 'torchscript'), ]  # (model, format)
 
 
 def run(cmd):

tests/test_python.py

@@ -77,6 +77,7 @@ def test_predict_img():
     seg_model = YOLO(WEIGHTS_DIR / 'yolov8n-seg.pt')
     cls_model = YOLO(WEIGHTS_DIR / 'yolov8n-cls.pt')
     pose_model = YOLO(WEIGHTS_DIR / 'yolov8n-pose.pt')
+    obb_model = YOLO(WEIGHTS_DIR / 'yolov8n-obb.pt')
     im = cv2.imread(str(SOURCE))
     assert len(model(source=Image.open(SOURCE), save=True, verbose=True, imgsz=32)) == 1  # PIL
     assert len(model(source=im, save=True, save_txt=True, imgsz=32)) == 1  # ndarray
@@ -105,6 +106,8 @@ def test_predict_img():
     assert len(results) == t.shape[0]
     results = pose_model(t, imgsz=32)
     assert len(results) == t.shape[0]
+    results = obb_model(t, imgsz=32)
+    assert len(results) == t.shape[0]
 
 
 def test_predict_grey_and_4ch():

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 @@ def save_crop(self, save_dir, file_name=Path('im.jpg')):
         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 @@ def pred_to_json(self, predn, filename):
                 '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 @@ def plot_training_samples(self, batch, ni):
                     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)