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Master YOLO with Ultralytics tutorials covering training, deployment and optimization. Find solutions, improve metrics, and deploy with ease!.

https://docs.ultralytics.com/guides/

[TanWeeKang]

hi, i am doing a project about food detection. the detection results were great. however, there are some overlapping bboxes for some categories of food. some input images will have this category and some will have others. how can i apply nms to retrieve only the highest conf score of the detection and remove overlapping bboxes? i run inference on my best.pt model by giving it input images

[TanWeeKang]

because the goal for now is to remove overlapping, if i set the iou threshold on the high side, the image doesnt differ much from the original detected image, where there are many overlappings. if i set to a low iou threshold, more overlappings will be removed.

[TanWeeKang]

another thing is that for my food detection i have 5 classes/categories of food. under these 5 categories i have variety of food for each category, but i label and train the model on these 5 classes, for simplicity sake i did not go into detail about what kind of food is that, lets say meat we have fish, pork etc. the model see either of those and classify as meat. although they are from same classes, they might have different prices. now how can i use segmentation( maybe from opencv) to distinguish the image i used to run inference on my best.pt model to identify the difference in meat like fish, pork to have different prices?

[glenn-jocher]

Hi there! Thanks for reaching out and providing detailed feedback on your experience with overlapping bounding boxes in your food detection project. It's fantastic to hear that your detection results are generally great! 🎉

To address the issue of overlapping bounding boxes, adjusting the IoU threshold is indeed a key factor. Here’s a more refined approach to help you achieve the best balance:

Fine-Tuning IoU Threshold

• High IoU Threshold: Retains more overlapping boxes, which might be useful if you want to ensure that no potential detections are missed.
• Low IoU Threshold: Removes more overlapping boxes, which can help in reducing redundancy but might risk missing some detections.

Practical Steps

1. Experiment with Intermediate Values: Instead of setting the IoU threshold to extremes, try intermediate values (e.g., 0.3, 0.4, 0.5) to find a sweet spot that minimizes overlaps without losing important detections.
2. Confidence Threshold: Adjust the confidence threshold (conf) to filter out low-confidence detections, which can also help in reducing overlaps.

Example Code

Here’s how you can apply these settings in Python:

from ultralytics import YOLO

# Load your custom-trained model
model = YOLO('path/to/best.pt')

# Run inference with custom NMS settings
results = model.predict(source='path/to/your/image.jpg', conf=0.25, iou=0.45)

# Display results
results.show()

And using the CLI:

yolo predict model=path/to/best.pt source='path/to/your/image.jpg' conf=0.25 iou=0.45

Additional Resources

For more detailed guidance on optimizing your model and handling common issues, you might find the following guides helpful:

• YOLO Common Issues
• YOLO Performance Metrics

If you continue to experience issues, please ensure you are using the latest versions of torch and ultralytics. If the problem persists, providing a minimum reproducible example would be very helpful. You can find guidelines on creating one here: Minimum Reproducible Example.

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

[TanWeeKang]

another thing is that for my food detection i have 5 classes/categories of food. under these 5 categories i have variety of food for each category, but i label and train the model on these 5 classes, for simplicity sake i did not go into detail about what kind of food is that, lets say meat we have fish, pork etc. the model see either of those and classify as meat. although they are from same classes, they might have different prices. now how can i use segmentation( maybe from opencv) to distinguish the image i used to run inference on my best.pt model to identify the difference in meat like fish, pork to have different prices?

[glenn-jocher]

Hi there! It's fantastic to hear about your progress with food detection using YOLO. 😊

To distinguish between different types of food within the same category (like fish and pork under "meat"), you can leverage segmentation techniques. Here's a step-by-step approach to achieve this:

1. Segmentation with OpenCV

You can use OpenCV to perform image segmentation, which will help you isolate different types of food within the same category. Here's a basic example of how you might use OpenCV for segmentation:

import cv2
import numpy as np

# Load the image
image = cv2.imread('path/to/your/image.jpg')

# Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Apply GaussianBlur to reduce noise and improve contour detection
blurred = cv2.GaussianBlur(gray, (5, 5), 0)

# Use Canny edge detection
edges = cv2.Canny(blurred, 50, 150)

# Find contours
contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# Draw contours on the original image
cv2.drawContours(image, contours, -1, (0, 255, 0), 2)

# Display the result
cv2.imshow('Segmented Image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

2. Combining YOLO Detection with Segmentation

You can combine the bounding boxes from your YOLO model with segmentation to further classify the detected objects. Here’s a conceptual approach:

1. Run YOLO Inference: Use your trained YOLO model to detect the general categories (e.g., meat).
2. Apply Segmentation: Within each detected bounding box, apply segmentation to isolate different types of food.
3. Classify Segmented Regions: Use additional classification models or heuristics to distinguish between types like fish and pork.

3. Advanced Segmentation Techniques

For more precise segmentation, consider using advanced models like Mask R-CNN or leveraging pre-trained segmentation models available in frameworks like PyTorch or TensorFlow.

Example with YOLO and Segmentation

Here’s a more integrated example combining YOLO detection with OpenCV segmentation:

from ultralytics import YOLO
import cv2

# Load your custom-trained YOLO model
model = YOLO('path/to/best.pt')

# Run inference
results = model.predict(source='path/to/your/image.jpg', conf=0.25, iou=0.45)

# Process each detected bounding box
for result in results:
    for box in result.boxes:
        x1, y1, x2, y2 = map(int, box.xyxy[0])
        cropped_img = result.orig_img[y1:y2, x1:x2]

        # Convert to grayscale and apply segmentation
        gray = cv2.cvtColor(cropped_img, cv2.COLOR_BGR2GRAY)
        blurred = cv2.GaussianBlur(gray, (5, 5), 0)
        edges = cv2.Canny(blurred, 50, 150)
        contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        cv2.drawContours(cropped_img, contours, -1, (0, 255, 0), 2)

        # Display the segmented image
        cv2.imshow('Segmented Image', cropped_img)
        cv2.waitKey(0)

cv2.destroyAllWindows()

Further Reading

For more detailed guides and advanced techniques, you might find these resources helpful:

• Isolating Segmentation Objects
• YOLO Thread-Safe Inference

Feel free to experiment with these techniques and adjust the parameters to better suit your specific use case. If you have any more questions or need further assistance, don't hesitate to ask. Happy coding! 🚀

[jdonza]

Hello,

Are there any tutorials showing how to train a YOLOv8 model using multiple devices for faster training? I read the instructions for YOLOv5 but got a bit lost. Step by step instructions would be greatly appreciated!

Thank you!

[glenn-jocher]

@jdonza the capability for multi-machine training is not supported in YOLOv8. The "Use Multiple Machines" section in the documentation you referenced may pertain to older versions or different contexts. For multi-GPU training on a single machine, you can use the device=[0,1] argument. If you have further questions, please refer to the Multi-GPU Training guide.

[jdonza]

Okay thank you!

[glenn-jocher]

@jdonza you're welcome! For training YOLOv8 on multiple GPUs within a single machine, you can use the device=[0,1] argument. Each GPU is identified by its ID, which you can check using nvidia-smi. Multi-machine training is not supported. For more details, please refer to the Multi-GPU Training guide.

[Ephraim-Bryski]

While not supported in YOLOv8, I did see reference to training across multiple computers in the YOLOv5 documentation:

(I believe this is what jdonza was referring to before). Does this allow for training across multiple computers for YOLOv5, but is no longer supported in YOLOv8? If so, would using YOLOv5 instead of v8 be recommended for this use case? Thanks!

[glenn-jocher]

Training across multiple computers is supported in YOLOv5 using DistributedDataParallel, but this feature is not available in YOLOv8. If multi-machine training is essential for your use case, using YOLOv5 would be recommended. For more details, please refer to the YOLOv5 documentation.

[jdonza]

I decided to make a different thread here as it is related to a different topic. But I wanted to know if you guys had documentation that went into the specifics of how the YOLO models are created. I am interested to learn more about CNN's, how they work, and how it relates to the image processing of YOLO.

[glenn-jocher]

@jdonza for detailed information on how YOLO models are created and how CNNs work in relation to YOLO, please refer to our comprehensive guides at https://docs.ultralytics.com/guides/. These resources cover various aspects of YOLO, including its architecture and image processing techniques.