datasets/detect/

[giscus[bot]]

datasets/detect/

Navigate through supported dataset formats, methods to utilize them and how to add your own datasets. Get insights on porting or converting label formats.

https://docs.ultralytics.com/datasets/detect/

[nepulepu]

Hello,
I have exported yolov8 dataset on roboflow but the format of the annotation seems off,
the annotation files looks like this:

0 0.4505104166666667 0.054072916666666665 0.4505104166666667 0.004177083333333333 0.3871354166666666 0.004177083333333333 0.3871354166666666 0.054072916666666665 0.4505104166666667 0.054072916666666665
0 0.48017708333333337 0.39927083333333335 0.48017708333333337 0.34398958333333335 0.43028125 0.34398958333333335 0.43028125 0.39927083333333335 0.48017708333333337 0.39927083333333335
0 0.5647291666666666 0.05663541666666667 0.5647291666666666 0.009708333333333334 0.50646875 0.009708333333333334 0.50646875 0.05663541666666667 0.5647291666666666 0.05663541666666667
0 0.44916666666666666 0 0.44916666666666666 0.028447916666666666 0.5111875 0.028447916666666666 0.5111875 0 0.44916666666666666 0
0 0.11178125 0.5493645833333333 0.11178125 0.49411458333333336 0.05559375 0.49411458333333336 0.05559375 0.5493645833333333 0.11178125 0.5493645833333333
0 0.09740625 0.18059375 0.09740625 0.11347916666666666 0.04121875 0.11347916666666666 0.04121875 0.18059375 0.09740625 0.18059375
0 0 0.0935 0.0285 0.0935 0.0285 0.020927083333333332 0 0.020927083333333332 0 0.0935
0 0.40507291666666667 0.2566041666666667 0.40507291666666667 0.21210416666666668 0.35517708333333337 0.21210416666666668 0.35517708333333337 0.2566041666666667 0.40507291666666667 0.2566041666666667
0 0.5711875000000001 0.6439687500000001 0.5711875000000001 0.5838125000000001 0.5115833333333334 0.5838125000000001 0.5115833333333334 0.6439687500000001 0.5711875000000001 0.6439687500000001

as far as I know the yolo format is : (class, center-x, center-y, width, height) and the annotation above has more than 5 points, when training the model it seems fine but what are the excessive points represent? does it affect model perfomance?

[pderrenger]

Hello,

It looks like the annotation you're seeing is more complex than the standard YOLO format because it includes coordinates for oriented bounding boxes (OBB), which are used to describe rotated rectangles. The standard YOLO format is indeed (class, center-x, center-y, width, height), but it seems your data might have been prepared with additional points for more detailed annotations, especially in scenarios where orientation matters, such as satellite imagery or certain types of scientific data.

This additional data shouldn't affect model performance negatively; it’s providing more detailed information that could be very useful if the model and the training setup are designed to utilize OBB. If you're training a regular YOLO model that expects standard bounding boxes, you'll want to convert these annotations or use a model that can interpret rotated bounding boxes.

To get a better understanding and potentially adjust your dataset annotations, you might find the section on porting or converting label formats in the Ultralytics Docs helpful.

👍 Happy training!

[nepulepu]

Hello,
Thanks for replying, I may know the reason this happens, as I import coco format annotation which has segmentation in the annotation file.
It causes the bbox become polygon, is there any way to export into normal yolo bbox or i need to re-edit my annotation files?
Thanks.

[pderrenger]

@nepulepu hello,

It looks like you've encountered an issue with the polygon bounding boxes derived from the segmentation data in your COCO format annotations. For training YOLO models, you'll typically need the annotations in the YOLO format, which expects bounding boxes as (class, center_x, center_y, width, height).

You can convert your polygon annotations to regular bounding boxes by calculating the bounding rectangle that encapsulates your polygon. Here’s a simple way you can modify your existing annotations:

1. For each polygon, find the minimum and maximum x and y coordinates.
2. Use these coordinates to calculate the bounding box’s center, width, and height.

Here’s a short example in Python, assuming you have polygon coordinates:

import numpy as np

# Example polygon coordinates
polygon = np.array([[x1, y1], [x2, y2], [x3, y3], [x4, y4]])

# Calculate bounding box
xmin, ymin = np.min(polygon, axis=0)
xmax, ymax = np.max(polygon, axis=0)
x_center = (xmin + xmax) / 2
y_center = (ymin + ymax) / 2
width = xmax - xmin
height = ymax - ymin

# Normalize by image dimensions if necessary
x_center /= image_width
y_center /= image_height
width /= image_width
height /= image_height

# YOLO format: class_id, x_center, y_center, width, height
yolo_bbox = [class_id, x_center, y_center, width, height]

Above, x1, y1, ... x4, y4 are the coordinates of the polygon’s vertices, and class_id is the object class. This would convert your segmentation polygons to the bounding boxes needed for YOLO.

If editing multiple files manually seems laborious, consider scripting this process or using tools designed to convert from COCO to YOLO format automatically, like the one mentioned in the Ultralytics documentation. This way, you can prepare your dataset efficiently for YOLO training.

Good luck with your model training! 👍

[skp4595]

just a follow up question here

I converted the dataset accordingly (images and it's labels), now I have used this class_mapping = {'Car': 0, 'Van': 1, 'Truck': 2, 'Pedestrian': 3, 'Person_sitting': 4, 'Cyclist': 5, 'Tram': 6, 'Misc': 7, 'DontCare': 8}.

Will the model weights be updated according to my class mapping or it'll update it's weights according to the class labels to which it was pretrained?

I mean to ask
let's say I mapped car as 0, but while pretraining you used car as 5, what will model predit after I train it on custom dataset?

[pderrenger]

Hi there!

Great question! When you train your model on a custom dataset with your own class mapping, the model will update its weights according to your new class labels. So, if you map 'Car' as 0, the model will learn to predict 'Car' as class 0, regardless of the class ID used during pretraining.

Here's a quick example to ensure your training setup:

from ultralytics import YOLO

# Load a pretrained model
model = YOLO("yolov8n.pt")

# Train the model with your custom dataset
results = model.train(data="your_dataset.yaml", epochs=100, imgsz=640)

Happy training! 🚀

[skp4595]

I'd like to contact you via linkedin or mail @ultralytics team.
I have a few more questions w.r.to the calibration. I tried the pretrained model and fine tuned model on kitti dataset, I'd like to discuss it over linkedin or mail.

[subbubhat99]

Hey there!
I'm trying to train RT-DETR-L on my custom dataset containing two classes of objects i.e. 0: 'Chewing Gum' and 1: 'Cigarette Butt'.
If I structure the dataset slightly differently i.e. having train, val and test folders with each of them having a data and labels sub-folders, why are my labels not being found by the code?
The YAML file contents look like:

path: path\to\root\directory #Path to the root directory of dataset
train: .\train\data #Path to the fraction of all images dedicated to training
val: .\val\data #Path to the fraction of all images dedicated to validation
test: .\test\data #Path to the fraction of all images dedicated to testing
train_labels: .\train\labels #Path to the fraction of all annotations dedicated to training
val_labels: .\val\labels #Path to the fraction of all annotations dedicated to validation
test_labels: .\test\labels #Path to the fraction of all annotations dedicated to testing
nc : 2 #Number of classes in the dataset
PS: I used absolute path notations for the root directory.

#Classes
names: ['Cigarette Butt', 'Chewing Gum']
Look forward to your support in this regard

[glenn-jocher]

Hello!

It looks like the issue might be due to the YAML file structure. In the current Ultralytics YOLO format, the paths for labels are not separately defined in the YAML file. Instead, the labels should be in the same directory as the images they correspond to, and the directory structure should be specified under the train, val, and test keys directly.

Here’s how you can adjust your YAML configuration:

path: path\\to\\root\\directory  # Path to the root directory of the dataset
train: train\\data  # Path to training images and labels
val: val\\data  # Path to validation images and labels
test: test\\data  # Path to testing images and labels
nc: 2  # Number of classes
names: ['Cigarette Butt', 'Chewing Gum']

Make sure that each image in train\\data, val\\data, and test\\data has a corresponding label file in the same directory. The label files should be named identically to the images, but with a .txt extension.

If your labels are still not being found, ensure that the paths are correctly specified and accessible from the directory where you are running your training script.

Hope this helps! Let us know if you have any more questions. 😊

[subbubhat99]

Hey Glenn!
Thanks for taking the time out to clarify that! I did end up changing the structure of my dataset to have two folders images and labels, each of which now have train, test and val sub-folders(similar to the format shown for coco.yaml). And that has worked.
Kind regards,
Subramanya

[glenn-jocher]

@subbubhat99 hello Subramanya,

Great to hear that adjusting your dataset structure resolved the issue! If you have any more questions or need further assistance as you continue with your project, feel free to reach out. Happy training! 😊

Kind