DOTA Dataset with OBB#

@XWP-PPP hi there!

DOTA's Task 1 server expects a zip of one text file per class (Task1_plane.txt, Task1_storage-tank.txt, ...), where each line is imgname score x1 y1 x2 y2 x3 y3 x4 y4, with the image name given without extension. OBB results are stored in result.obb, not result.boxes. Here is a snippet that builds those files:

import shutil
from collections import defaultdict
from pathlib import Path

from ultralytics import YOLO

# Load your trained YOLO26-OBB model
model = YOLO("path/to/your/yolo26n-obb.pt")

# Collect one DOTA Task1 line per detection: imgname score x1 y1 x2 y2 x3 y3 x4 y4 (pixels)
category_results = defaultdict(list)
for r in model.predict(source="path/to/test/images", stream=True):
    img_name = Path(r.path).stem
    for cls, conf, pts in zip(r.obb.cls.int().tolist(), r.obb.conf.tolist(), r.obb.xyxyxyxy.flatten(1).tolist()):
        coords = " ".join(f"{p:.1f}" for p in pts)
        category_results[f"Task1_{model.names[cls].replace(' ', '-')}.txt"].append(f"{img_name} {conf:.6f} {coords}\n")

# Save one file per category and zip the submission folder
output_dir = Path("submission")
output_dir.mkdir(exist_ok=True)
for category_file, lines in category_results.items():
    (output_dir / category_file).write_text("".join(lines))
shutil.make_archive("submission", "zip", output_dir)

Upload submission.zip to the DOTA Task 1 server. If your mAP differs from your local validation, check that image names match the test set and that the coordinates are in pixels of the original (unsplit) images. For more details, see the DOTA Dataset Documentation. Happy coding! 😊

@keeper-jie for training YOLO26-OBB on DOTAv1 after splitting, use this command with your 4 GPUs:
yolo obb train data=DOTAv1.yaml model=yolo26n-obb.pt epochs=500 imgsz=1024 batch=128 device=4,5,6,7

Key parameters from our DOTA documentation:

• imgsz=1024 (matches dataset's multiscale split)
• Use official model name yolo26n-obb.pt rather than YAML path
• Batch size 128 (32*4 GPUs) via batch=128

Ensure your dataset YAML paths point to the split images directory.

To add the angle value from your existing YOLOv8 labels, you'll need to calculate it based on the coordinates. Unfortunately, without the original CVAT labels, this requires manual computation or using a tool that supports angle annotations. For guidance on datasets with oriented bounding boxes, you can refer to the DOTA dataset documentation at https://docs.ultralytics.com/datasets/obb/dota-v2/.

@jack-ready hi there!

Thank you for reaching out with your questions regarding training the DOTA dataset with YOLOv8. Let's address each of your concerns:

1. Image Splitting and Sizes: When splitting high-resolution DOTA images into 1024x1024 segments using rates=[0.5, 1.0, 1.5], you might notice some resulting images are slightly smaller than 1024x1024 (e.g., 1024x1023 or 824x1024). This occurs due to the way the splitting algorithm handles the edges of the original images. If the original image dimensions are not perfectly divisible by 1024, the last segment in each row or column might be smaller. This should not significantly affect your experiment as long as the majority of your data maintains the expected size. However, you can ensure consistency by padding these smaller images to 1024x1024 if necessary.

2. Disabling Validation: The val=False argument should indeed disable validation during training. If it doesn't seem to work, please ensure you are using the latest version of the Ultralytics package. You can update it using:

   pip install --upgrade ultralytics

   If the issue persists, providing a minimum reproducible example would be very helpful for us to diagnose the problem. You can find guidance on creating one here: Minimum Reproducible Example.

3. Image Resizing During Validation: The discrepancy in image sizes during validation (e.g., torch.Size([10, 3, 672, 672]) instead of 640) might be due to the model's internal resizing logic. Ensure that the imgsz parameter is correctly set for both training and validation. Validation uses the same imgsz as training, so set it explicitly in your training script:

   results = model.train(data="DOTAv1.yaml", epochs=100, imgsz=640, batch=10, workers=0, val=True)

I hope this helps! If you have any further questions or need additional assistance, feel free to ask. Happy training! 🚀

Thank you very much for your help. I have another question regarding the YOLOV8 training on the DOTA v1.0 dataset. I would like to know how to set the training parameters, such as learning rate, optimizer, and loss function. When I try to reproduce the results, my mAP₅₀ is about 74%, but it does not reach 78%. I would greatly appreciate any assistance you can provide.Below is my training code. from ultralytics import YOLO model = YOLO("yolov8n-obb.yaml") model.load("yolov8n-obb.pt") model = YOLO("yolov8n-obb.yaml").load("yolov8n.pt") results = model.train(data="DOTAv1.yaml", epochs=100, imgsz=640, batch=48, workers=0,project="runs/DOTAV1.0",val=True,resume=True)

Hi there! Glad to help with your training setup. To fine-tune your training parameters for better mAP₅₀ results, you can adjust the learning rate, optimizer, and loss function. For instance, you might try a lower learning rate or switch to a different optimizer like Adam. Here's a quick example:

from ultralytics import YOLO

model = YOLO("yolo26n-obb.yaml").load("yolo26n-obb.pt")
results = model.train(
    data="DOTAv1.yaml", 
    epochs=100, 
    imgsz=640, 
    batch=48, 
    workers=0, 
    project="runs/DOTAV1.0", 
    val=True, 
    lr0=0.001,  # Adjust learning rate
    optimizer='Adam'  # Change optimizer
)

Ensure you're using the latest version of the Ultralytics package to avoid any bugs. If you still face issues, feel free to share more details. Happy training! 🚀

@Tintin26100 hello!

The number of instances per class in DOTA v1.5 isn't directly listed in the dataset's summary. However, you can obtain this information by analyzing the annotation files provided with the dataset. Each object instance in these files is labeled with a class, and you can count the number of times each class appears.

If you need further guidance on how to process these annotation files or any other assistance with the DOTA dataset, feel free to ask!

Happy coding! 😊

Hi, thanks for your quick answer, I will gladly accept your help because I don't really know what to do to extract those informations, where do you find the annotation files ? Is it on the github ? If it is what's it's path ? Thanks for the time you spend helping

@Tintin26100 hello!

The annotation files for the DOTA dataset are typically included within the dataset download itself. These files contain the details for each instance, including class labels and oriented bounding box coordinates.

For the DOTA v1.5 dataset, you can find these files in the dataset directory after you download it from the official DOTA dataset page or the provided download link in the dataset's YAML configuration file. The path to the annotations will usually be structured under the labels or annotations directory within the main dataset folder.

If you've already downloaded the dataset, you should look for .txt or .json files corresponding to each image in the dataset, which contain the annotations. If you need further assistance navigating these files or extracting specific data, feel free to ask!

Thank you for reaching out, and happy analyzing! 😊

Hey there! 😊 Testing the DOTA dataset online using YOLO26-obb is quite straightforward. If you've already prepared your dataset according to the Ultralytics guidelines and split the images, here's a simple example to get you started with training:

# Train a YOLO26n-OBB model on the DOTA v1 dataset
yolo obb train data=DOTAv1.yaml model=yolo26n-obb.pt epochs=100 imgsz=640

This command uses DOTA v1 and the corresponding .yaml file (DOTAv1.yaml) describing your dataset paths and classes. Adjust epochs and imgsz based on your needs.

Just make sure to check out the DOTA dataset page on Ultralytics docs for more info on dataset preparation and additional configurations! Happy detecting! 🚀