Comments (4)
👋 Hello @sivaramakrishnan-rajaraman, thank you for your interest in Ultralytics YOLOv8 🚀! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.
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Install
Pip install the ultralytics
package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.
pip install ultralytics
Environments
YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
- Notebooks with free GPU:
- Google Cloud Deep Learning VM. See GCP Quickstart Guide
- Amazon Deep Learning AMI. See AWS Quickstart Guide
- Docker Image. See Docker Quickstart Guide
Status
If this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.
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@sivaramakrishnan-rajaraman hi there,
Thank you for reaching out! To include bounding box coordinates (x-center, y-center, width, height) in the label files during prediction with your YOLOv8 instance segmentation model, you can modify the code to save these additional details.
While the CLI currently does not directly support adding bounding box coordinates to the label files, you can achieve this by using the Python API. Below is an example of how you can modify your prediction script to include the bounding box coordinates in the label files:
from ultralytics import YOLO
# Load the model
model = YOLO('/weights/best.pt')
# Run prediction
results = model.predict(source='/test/images', conf=0.25, imgsz=1024, save=True, save_txt=True, save_conf=True)
# Save predictions with bounding box coordinates
for result in results:
for i, (box, mask, conf, cls) in enumerate(zip(result.boxes.xywh, result.masks.xy, result.boxes.conf, result.boxes.cls)):
# Prepare the label content
label_content = f"{int(cls)} " + " ".join(map(str, mask.flatten().tolist())) + f" {conf:.6f} " + " ".join(map(str, box.tolist()))
# Save to file
label_file = f"{result.path.stem}_{i}.txt"
with open(label_file, 'w') as f:
f.write(label_content)
This script will save the bounding box coordinates along with the polygonal coordinates and confidence scores in the label files.
If you prefer to stick with the CLI, you might need to run the predictions first and then post-process the results to add the bounding box coordinates to the label files. However, using the Python API as shown above provides a more streamlined approach.
Please ensure you are using the latest versions of torch
and ultralytics
to avoid any compatibility issues. You can update your packages using:
pip install --upgrade torch ultralytics
For further details, you can refer to the Ultralytics documentation.
Feel free to reach out if you have any more questions. Happy coding! 😊
from ultralytics.
Thanks, it works this way :)
from ultralytics.
Hi @sivaramakrishnan-rajaraman,
I'm glad to hear that it worked for you! 😊 If you have any more questions or run into any other issues, feel free to reach out. We're here to help!
Happy coding and best of luck with your project!
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Related Issues (20)
- Train with single gpu HOT 3
- Yolo8-OnnxRuntime-CPP-Inference awful output HOT 6
- confusion matrix single HOT 2
- Class imabalance dataloader HOT 1
- Replace confidence score for forward pass for. yolov8. Default is 0.25 HOT 5
- The Yolov8 model is wrong in predicting probability HOT 9
- Superfluous line in Model HOT 2
- Re train yolov8n.pt to detect more objects from a custom dataset? HOT 12
- image 1/1 D:\yolov8\ultralytics-main\ultralytics\assets\bus.jpg: 640x480 (no detections), 510.2ms Speed: 15.5ms preprocess, 510.2ms inference, 18.0ms postprocess per image at shape (1, 3, 640, 480) HOT 4
- How to Shut Down Wandb HOT 1
- Issues with using dataset which is not is square dimensions. HOT 4
- Whether to support anchor-base HOT 3
- How can i plot the loss and mAP diagram after training yolov8 ? HOT 2
- YOLOv10 NCNN export HOT 2
- segmentation HOT 1
- unexpected freezed layer HOT 4
- KeyError When Customization to YOLOv8 Model: HOT 9
- YOLOv10 export: Setting simplify=True raise exception HOT 7
- TensorFlow & tflite Export Not Working HOT 6
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