I figured out using ChatGPT. However, I am looking at the imbalance issue.

@XiFromEY hey there! 👋 To focus on detecting buildings and damaged buildings, you'll want to modify your dataset's classes. The simplest way would be to create a script to update your *.txt labels to delete any rows that don't pertain to the classes you are interested in, and then train normally with your updated YAML that points to your modified labels:

yolo detect train data=xView.yaml model=yolo26n.pt epochs=100 imgsz=640

This will train your model to detect just the building classes you're interested in. Happy coding! 🚀

The conversion code is already bundled in the download: section of the dataset YAML—see the maintained xView.yaml conversion script (also referenced from the xView dataset docs); place your manual download as datasets/xView/train_images/, datasets/xView/val_images/, and datasets/xView/xView_train.geojson (not under images/), then run:

yolo detect train data=xView.yaml model=yolo26n.pt imgsz=640 epochs=1

If it still fails, please paste the full text traceback here (not a screenshot) and your yolo checks output so we can pinpoint what’s off in the paths/GeoJSON.

manual download as datasets/xView/train_images/, datasets/xView/val_images/, and datasets/xView/xView_train.geojson after run yolo detect train data=xView.yaml model=yolo26n.pt imgsz=640 epochs=1 can it formated or i need to do manually convert it?

i am getting this error. (.new) (base) alpha@alpha-HP-Z2-Tower-G9-Workstation-Desktop-PC:~/Desktop/chakma/test$ python train.py Ultralytics 8.4.7 🚀 Python-3.11.7 torch-2.9.1+cu128 CUDA:0 (NVIDIA GeForce RTX 3090, 24115MiB) engine/trainer: agnostic_nms=False, amp=True, angle=1.0, augment=False, auto_augment=randaugment, batch=16, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, compile=False, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=xView.yaml, degrees=0.0, deterministic=True, device=None, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=100, erasing=0.4, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=640, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.01, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=yolov8n.pt, momentum=0.937, mosaic=1.0, multi_scale=0.0, name=train16, nbs=64, nms=False, opset=None, optimize=False, optimizer=auto, overlap_mask=True, patience=100, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=None, rect=False, resume=False, retina_masks=False, rle=1.0, save=True, save_conf=False, save_crop=False, save_dir=/home/alpha/Desktop/chakma/test/runs/detect/train16, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.5, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None

WARNING ⚠️ Dataset 'xView.yaml' images not found, missing path '/home/alpha/Desktop/chakma/test/datasets/xView/images/autosplit_val.txt' Traceback (most recent call last): File "/home/alpha/Desktop/chakma/test/.new/lib/python3.11/site-packages/ultralytics/engine/trainer.py", line 660, in get_dataset data = check_det_dataset(self.args.data) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/alpha/Desktop/chakma/test/.new/lib/python3.11/site-packages/ultralytics/data/utils.py", line 471, in check_det_dataset exec(s, {"yaml": data}) File "", line 9, in ImportError: cannot import name 'autosplit' from 'ultralytics.data.utils' (/home/alpha/Desktop/chakma/test/.new/lib/python3.11/site-packages/ultralytics/data/utils.py)

The above exception was the direct cause of the following exception:

Traceback (most recent call last): File "/home/alpha/Desktop/chakma/test/train.py", line 7, in results = model.train(data="xView.yaml", epochs=100, imgsz=640) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/alpha/Desktop/chakma/test/.new/lib/python3.11/site-packages/ultralytics/engine/model.py", line 769, in train self.trainer = (trainer or self._smart_load("trainer"))(overrides=args, _callbacks=self.callbacks) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/alpha/Desktop/chakma/test/.new/lib/python3.11/site-packages/ultralytics/models/yolo/detect/train.py", line 63, in init super().init(cfg, overrides, _callbacks) File "/home/alpha/Desktop/chakma/test/.new/lib/python3.11/site-packages/ultralytics/engine/trainer.py", line 186, in init self.data = self.get_dataset() ^^^^^^^^^^^^^^^^^^ File "/home/alpha/Desktop/chakma/test/.new/lib/python3.11/site-packages/ultralytics/engine/trainer.py", line 664, in get_dataset raise RuntimeError(emojis(f"Dataset '{clean_url(self.args.data)}' error ❌ {e}")) from e RuntimeError: Dataset 'xView.yaml' error ❌ cannot import name 'autosplit' from 'ultralytics.data.utils' (/home/alpha/Desktop/chakma/test/.new/lib/python3.11/site-packages/ultralytics/data/utils.py) (.new) (base) alpha@alpha-HP-Z2-Tower-G9-Workstation-Desktop-PC:~/Desktop/chakma/test$

That ImportError: cannot import name 'autosplit' means you’re running a stale xView.yaml download: script that imports autosplit from the wrong module (it’s why images/autosplit_val.txt never gets created), so you don’t need to manually convert—just update Ultralytics and ensure you’re using the xView.yaml that ships with your installed package (not an older local copy); the dataset conversion is triggered automatically by check_det_dataset() when files are missing (see the dataset check/download logic).

pip install -U ultralytics
yolo checks
yolo detect train data=xView.yaml model=yolo26n.pt imgsz=640 epochs=1

If it still fails, paste your yolo checks output and the first ~15 lines of the download: block from the exact xView.yaml file your run is loading (plus the full traceback).

thanks finally it working. i trained on yolov8n the result is quite low. map50 and map50-95 and recall low but prec is high is it normal or have some issue.here is the script and the result.

import os os.environ['OPENCV_LOG_LEVEL'] = 'ERROR' import warnings warnings.filterwarnings('ignore') from ultralytics import YOLO

Load a model

model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)

Train the model

results = model.train(data="xView.yaml", epochs=100, imgsz=640, batch=4,amp=True,workers=4,cache=False)

====================

Validating /home/alpha/Desktop/chakma/test/runs/detect/train46/weights/best.pt... Ultralytics 8.4.7 🚀 Python-3.11.7 torch-2.9.1+cu128 CUDA:0 (NVIDIA GeForce RTX 3090, 24115MiB) Model summary (fused): 73 layers, 3,017,348 parameters, 0 gradients, 8.1 GFLOPs Class Images Instances Box(P R mAP₅₀ mAP_50-95): 18% ━━────────── 2/11 1.1s/it 0.7s<9.8sWARNING ⚠️ NMS time limit 2.400s exceeded Class Images Instances Box(P R mAP₅₀ mAP_50-95): 100% ━━━━━━━━━━━━ 11/11 1.2it/s 9.5s all 86 64690 0.61 0.00241 0.0044 0.00219 Fixed-wing Aircraft 4 5 0 0 0 0 Small Aircraft 7 144 0 0 0 0 Cargo Plane 16 127 1 0 0 0 Helicopter 5 10 1 0 0 0 Passenger Vehicle 22 114 1 0 0 0 Small Car 71 24345 0 0 0 0 Bus 45 645 1 0 0 0 Pickup Truck 25 156 1 0 0 0 Utility Truck 33 410 0 0 0 0 Truck 49 1536 1 0 0 0 Cargo Truck 48 666 1 0 0 0 Truck w/Box 29 343 0 0 0 0 Truck Tractor 17 39 1 0 0 0 Trailer 36 977 1 0 0 0 Truck w/Flatbed 21 85 1 0 0 0 Truck w/Liquid 5 9 0 0 0 0 Crane Truck 8 17 1 0 0 0 Railway Vehicle 1 2 1 0 0 0 Passenger Car 5 72 1 0 0 0 Cargo Car 4 23 0 0 0 0 Locomotive 1 1 1 0 0 0 Maritime Vessel 10 80 1 0 0 0 Motorboat 11 47 0 0 0 0 Sailboat 2 2 1 0 0 0 Tugboat 4 25 1 0 0 0 Barge 5 25 0 0 0 0 Fishing Vessel 11 115 0 0 0 0 Ferry 7 33 0 0 0 0 Yacht 4 7 1 0 0 0 Container Ship 5 31 1 0 0 0 Oil Tanker 5 9 0 0 0 0 Engineering Vehicle 13 27 1 0 0 0 Tower crane 8 18 0 0 0 0 Container Crane 1 1 0 0 0 0 Reach Stacker 3 8 1 0 0 0 Straddle Carrier 1 1 1 0 0 0 Mobile Crane 11 34 0 0 0 0 Dump Truck 29 120 1 0 0 0 Haul Truck 3 33 1 0 0 0 Scraper/Tractor 3 3 0 0 0 0 Front loader/Bulldozer 23 86 0 0 0 0 Excavator 15 97 1 0 0 0 Cement Mixer 7 11 1 0 0 0 Ground Grader 4 5 1 0 0 0 Hut/Tent 8 40 1 0 0 0 Shed 24 124 1 0 0 0 Building 79 32296 0.398 0.14 0.199 0.0823 Aircraft Hangar 3 13 0 0 0 0 Damaged Building 14 98 0 0 0 0 Facility 21 85 1 0 0 0 Construction Site 20 160 1 0 0 0 Vehicle Lot 39 597 1 0 0 0 Helipad 4 5 0 0 0 0 Storage Tank 23 132 0 0 0 0 Shipping container lot 15 404 1 0 0.0557 0.0446 Shipping Container 14 174 0 0 0 0 Pylon 7 15 1 0 0 0 Tower 3 3 1 0 0 0 Speed: 0.4ms preprocess, 9.2ms inference, 0.0ms loss, 66.1ms postprocess per image Results saved to /home/alpha/Desktop/chakma/test/runs/detect/train46

Yes—on xView this “high precision, near-zero recall/mAP” usually means the model is producing very few usable detections, and your WARNING ⚠️ NMS time limit ... exceeded during val is a strong hint the evaluation is getting truncated on these very dense scenes (lots of tiny objects per image), so the reported metrics can look artificially awful; the most effective fix is to train/validate on tiled crops (so each image has fewer objects) and/or increase imgsz, and for a stronger baseline start from yolo26n.pt/yolo26s.pt rather than yolov8n.pt (see the relevant knobs in the Train mode docs).

Those are OpenCV/libtiff GeoTIFF metadata warnings from reading xView .tif files (not a YOLO error), so if training continues you can ignore them; if your run actually stops, please paste the full text traceback plus the output of:

yolo checks

If you just want to silence the warnings, you can run with:

OPENCV_LOG_LEVEL=ERROR yolo detect train data=xView.yaml model=yolo26n.pt imgsz=1024

and if OpenCV can’t reliably read your TIFFs on your system, a practical workaround is to re-encode the .tif to plain RGB (PNG/JPG) after confirming your folder layout matches the xView dataset guide.

thank you very. so it will not effect model perfomance?

Yes—those GeoTIFF tag warnings are about metadata parsing and won’t affect model performance as long as the images decode correctly and training/validation completes; if you want to sanity-check decoding, run a quick inference on a few .tif tiles (and if anything looks off, paste the full traceback + yolo checks output as in the Troubleshooting Common YOLO Issues guide).

@Mahmoud-owais it looks like you're encountering an "Out of Memory" error during training. This can happen if the batch size or image size is too large for your GPU's memory. Try reducing the batch size or imgsz to see if that resolves the issue. Additionally, ensure you're using the latest version of the Ultralytics package by running pip install -U ultralytics. If the problem persists, please let us know. For more details on the xView dataset, you can visit xView Dataset Documentation.

@Mahmoud-owais the warning indicates that no labels were found in the specified directory for the xView dataset. This could be due to incorrect dataset formatting or missing label files. Please ensure that your dataset is correctly formatted and that the label files are in the expected location. For detailed guidance on dataset formatting, refer to the Ultralytics dataset documentation. Additionally, updating to the latest version of the Ultralytics package might resolve any underlying issues. You can update using pip install -U ultralytics.

thanks datasets/ └── xView/ ├── images/ │ ├── train/ │ └── val/ ├── labels/ │ ├── train/ │ └── val/ i have the dataset and xView_train.geojson how to by xView.yaml to extract the xView_train.geojson to this structure datasets/ └── xView/ ├── images/ │ ├── train/ │ └── val/ ├── labels/ │ ├── train/ │ └── val/

and i found this Error Transferred 319/355 items from pretrained weights TensorBoard: Start with 'tensorboard --logdir runs/detect/train28', view at http://localhost:6006/ Freezing layer 'model.22.dfl.conv.weight' train: Scanning /content/drive/MyDrive/ultralytics-main/datasets/xView/labels/train... 0 images, 760 backgrounds, 3 corrupt: 100%|██████████| 763/763 [06:10<00:00, 2.06it/s]train: WARNING ⚠️ /content/drive/MyDrive/ultralytics-main/datasets/xView/images/train/._100.tif: ignoring corrupt image/label: cannot identify image file '/content/drive/MyDrive/ultralytics-main/datasets/xView/images/train/._100.tif' train: WARNING ⚠️ /content/drive/MyDrive/ultralytics-main/datasets/xView/images/train/._102.tif: ignoring corrupt image/label: cannot identify image file '/content/drive/MyDrive/ultralytics-main/datasets/xView/images/train/._102.tif' train: WARNING ⚠️ /content/drive/MyDrive/ultralytics-main/datasets/xView/images/train/._109.tif: ignoring corrupt image/label: cannot identify image file '/content/drive/MyDrive/ultralytics-main/datasets/xView/images/train/._109.tif' train: WARNING ⚠️ No labels found in /content/drive/MyDrive/ultralytics-main/datasets/xView/labels/train.cache. See https://docs.ultralytics.com/datasets for dataset formatting guidance.

train: New cache created: /content/drive/MyDrive/ultralytics-main/datasets/xView/labels/train.cache WARNING ⚠️ No labels found in /content/drive/MyDrive/ultralytics-main/datasets/xView/labels/train.cache, training may not work correctly. See https://docs.ultralytics.com/datasets for dataset formatting guidance. albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8)) val: Scanning /content/drive/MyDrive/ultralytics-main/datasets/xView/labels/train... 0 images, 86 backgrounds, 0 corrupt: 100%|██████████| 86/86 [00:41<00:00, 2.07it/s]val: WARNING ⚠️ No labels found in /content/drive/MyDrive/ultralytics-main/datasets/xView/labels/train.cache. See https://docs.ultralytics.com/datasets for dataset formatting guidance. val: New cache created: /content/drive/MyDrive/ultralytics-main/datasets/xView/labels/train.cache WARNING ⚠️ No labels found in /content/drive/MyDrive/ultralytics-main/datasets/xView/labels/train.cache, training may not work correctly. See https://docs.ultralytics.com/datasets for dataset formatting guidance.

Plotting labels to runs/detect/train28/labels.jpg... zero-size array to reduction operation maximum which has no identity optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... optimizer: AdamW(lr=0.000156, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0) TensorBoard: model graph visualization added ✅ Image sizes 640 train, 640 val Using 0 dataloader workers Logging results to runs/detect/train28 Starting training for 100 epochs...

@Mahmoud-owais it looks like the issue is related to corrupt image files and missing labels in your dataset. The warnings indicate that some images in your training set are not being recognized, and no labels were found in the specified directory. Please ensure that your dataset is correctly formatted and that all necessary label files are present. You can refer to the Ultralytics dataset documentation for detailed guidance on proper dataset formatting. Additionally, make sure to check if the issue persists with the latest version of the Ultralytics package.

@goodbbboy hello!

To split the original xView images into 800x800 resolution tiles similar to the DOTA dataset, you'll need to modify the ultralytics/data/split_dota.py script to handle the xView dataset format. Here are some steps to guide you through the process:

1. Modify the Splitting Logic: Adjust the script to read the xView images and split them into 800x800 tiles. Ensure that the splitting logic handles the overlap and edge cases properly.

2. Adjust Label Conversion: Since the xView labels are in a different format, you'll need to convert them to match the DOTA format. This involves parsing the xView .txt labels and transforming them into the required format.

Here's a basic example to get you started:

import os
from PIL import Image
import numpy as np

def split_image(image_path, output_dir, tile_size=800, overlap=200):
    image = Image.open(image_path)
    image_name = os.path.basename(image_path).split('.')[0]
    width, height = image.size

    for i in range(0, width, tile_size - overlap):
        for j in range(0, height, tile_size - overlap):
            box = (i, j, i + tile_size, j + tile_size)
            tile = image.crop(box)
            tile.save(os.path.join(output_dir, f"{image_name}_{i}_{j}.png"))

def convert_labels(xview_label_path, output_dir, tile_size=800, overlap=200):
    with open(xview_label_path, 'r') as file:
        lines = file.readlines()

    for line in lines:
        # Parse the xView label format and convert to DOTA format
        # This is a placeholder; you'll need to implement the actual conversion logic
        pass

# Example usage
split_image('path/to/xview/image.tif', 'output/tiles')
convert_labels('path/to/xview/label.txt', 'output/labels')

1. Integrate with Ultralytics YOLO: Ensure that the modified script integrates well with the Ultralytics YOLO training pipeline. You might need to adjust the dataset YAML file to point to the new split images and labels.

For more detailed guidance on creating a minimum reproducible example, please refer to our Minimum Reproducible Example documentation. Additionally, make sure you are using the latest version of the Ultralytics packages to benefit from the latest features and bug fixes.

Feel free to reach out if you have any further questions. Happy coding! 🚀

Hello! 👋 It looks like you're encountering an issue with the automatic download of the xView dataset. The xView dataset requires manual downloading due to its size and licensing.

For this dataset, you need to:

1. Visit the xView dataset website and download the dataset files manually.
2. Follow the instructions for downloading train_images.zip and val_images.zip.
3. Unzip these files under 'datasets/xView' relative to your working directory.

After downloading and organizing the dataset, your file structure should look something like this:

ultralytics/
└── datasets/
    └── xView/
        ├── train_images/
        └── val_images/

Make sure to place xView.yaml correctly in your project, specifying this file structure.

If you have further issues or questions, feel free to ask. Happy training! 🚀