Ensemble Models

[Caliphamin]

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Hello everyone I am working on projects that has 3 classes Now i have 2 model which one of them trained with yolov8x-seg, imgsize=640 and the other model trained with yolov8s-seg,imgsize=1024 I want to ensemble one of the classes for example i want to ensemble floor class with x and s model I want to know that this option available on yolo library? Or what is the easiest way to ensemble? Is there a library for instance segmentation ensemble?

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[glenn-jocher]

@Caliphamin hello!

YOLOv8, much like its predecessors, does not inherently include a built-in mechanism for ensembling models, especially when those models are of different sizes or have been trained on diverse tasks or data. Ensembling diverse models, such as combining detections from a yolov8x-seg and yolov8s-seg model, requires custom implementation outside of the existing YOLOv8 toolset.

For ensembling object detection and instance segmentation predictions, you'd typically need to write code that takes the predictions from each model and intelligently merges them. The process would involve:

1. Running inference separately with each model and obtaining the predictions for your class of interest ("floor" in your case).
2. Applying a technique to combine these predictions, taking into account factors like detection confidence scores, IoU (Intersection over Union) thresholds, and possibly class probabilities.

Common strategies for ensembling models include:

• Non-Maximum Suppression (NMS) across the results of all models.
• Weighted boxes fusion where each box is assigned a weight based on the model confidence.
• Consensus-based approaches, where only detections agreed upon by multiple models are kept.

For instance segmentation, the process can be even more complex since you are dealing with pixel-level predictions. The basic principles are similar, but you'll need specialized methods to combine segmentation masks.

The actual implementation of model ensembling does require a fair amount of coding and a good understanding of both the models' outputs and how they should be best combined. Currently, there are no dedicated libraries within the YOLOv8 ecosystem for this task. You may find generic ensembling libraries or could develop your own scripts to handle this process.

For more specific guidance on available functionality within YOLOv8, refer to our documentation where we aim to provide comprehensive details on the features and use cases supported by Ultralytics models.

[Caliphamin]

@glenn-jocher thanks for replying i have another question why the model x and model s on the same image prediction creates different mask shape? for example i have 1.jpg predict with model x (which trained on 640 imgsize) generates shape of [448, 640] predict with model s (which trained on 1024 imgsize) generates shape of [672, 1024] how can i reshape model s to model x shape or model x to model s

[glenn-jocher]

@Caliphamin when you are working with models that have been trained at different image sizes, it's expected that the output dimension of the segmentation masks will be different, as each model is tailored towards its respective input resolution. The yolov8x-seg trained at an image size of 640 pixels will output a mask that corresponds to that scale, while the yolov8s-seg trained at 1024 pixels will produce a mask with dimensions scaled accordingly.

To align the masks from both models, you would need to resize one mask to match the dimensions of the other. However, it is important to be cautious when resizing segmentation masks as interpolation methods might affect the mask boundaries and the quality of the segmentation.

If you wish to standardize the masks, you would typically:

1. Choose the target dimension (either 640x448 as in yolov8x-seg or 1024x672 as in yolov8s-seg).
2. Use an image processing library to resize the mask from the source dimension to the target dimension.
3. Apply the resizing uniformly across all masks for consistency.

For example, to resize the mask from yolov8s-seg (1024) to yolov8x-seg (640), you would downsample the mask. Conversely, to match yolov8x-seg (640) to yolov8s-seg (1024), you'd upsample the mask. Keep in mind that resizing can introduce artifacts or smooth out details. Bilinear or nearest-neighbor interpolations are commonly used methods for resizing masks.

Keep the aspect ratio consistent and ensure that after resizing, the masks still accurately represent the regions of interest in the image. Fine-tuning the resized masks further may be necessary, depending on the application's requirements and quality expectations.

[Caliphamin]

@glenn-jocher oh i see now So using cv2.resize() could help me to downsample my mask? And also i should use methods like nearest neighbor

[glenn-jocher]

@Caliphamin yes, using cv2.resize() from OpenCV can indeed help you to resize your masks. You can specify the interpolation method in the function call. For resizing segmentation masks, cv2.INTER_NEAREST, which corresponds to nearest-neighbor interpolation, is often recommended because it doesn't introduce new colors in the mask, which can happen with other interpolation methods like bilinear or cubic.

Nearest-neighbor interpolation ensures that the resized mask only contains pixel values that were present in the original mask, which is particularly important for instance segmentation where each pixel label has semantic meaning. This approach is more appropriate for categorical data, such as masks, where the integrity of the classes needs to be maintained after the resizing operation.

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