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Welcome to YOLOv8 Explainer

Simplify your understanding of YOLOv8 Results

This is a package with state of the art Class Activated Mapping(CAM) methods for Explainable AI for computer vision using YOLOv8. This can be used for diagnosing model predictions, either in production or while developing models. The aim is also to serve as a benchmark of algorithms and metrics for research of new explainability methods.

Install Environment & Dependencies

YOLOv8-Explainer can be seamlessly integrated into your projects with a straightforward installation process:

Installation as a Package

To incorporate YOLOv8-Explainer into your project as a dependency, execute the following command in your terminal:

pip install YOLOv8-Explainer

Features and Functionality

YOLOv8-Explainer can be used to deploy various different CAM models for cutting-edge XAI methodologies in YOLOv8 for images:

  • GradCAM : Weight the 2D activations by the average gradient
  • GradCAM + + : Like GradCAM but uses second order gradients
  • XGradCAM : Like GradCAM but scale the gradients by the normalized activations
  • EigenCAM : Takes the first principle component of the 2D Activations (no class discrimination, but seems to give great results)
  • HiResCAM : Like GradCAM but element-wise multiply the activations with the gradients; provably guaranteed faithfulness for certain models
  • LayerCAM : Spatially weight the activations by positive gradients. Works better especially in lower layers
  • EigenGradCAM : Like EigenCAM but with class discrimination: First principle component of Activations*Grad. Looks like GradCAM, but cleaner

Using from code as a library

from YOLOv8_Explainer import yolov8_heatmap, display_images

model = yolov8_heatmap(
    weight="/location/model.pt", 
        conf_threshold=0.4, 
        device = "cpu", 
        method = "EigenCAM", 
        layer=[12, 17, 21],
        ratio=0.02,
        show_box=True,
        renormalize=False,
)

images = model(
    img_path="/location/image.jpg", 
    )

display_images(images)
- Here the from YOLOv8_Explainer import yolov8_heatmap, display_images allows you to import the required functionalities.

  • The line model = yolov8_heatmap( weight="/location/model.pt", conf_threshold=0.4, device = "cpu", method = "EigenCAM", layer=[12, 17, 21], ratio=0.02, show_box=True, renormalize=False) allows the user to pass a pertrained YOLO weight which the CAM is expected to evaluate, along with additional parameters like the desired CAM method target layers, and the confidence threshold.

You can choose between the following CAM Models for version 0.0.5:

GradCAM , HiResCAM, GradCAMPlusPlus, XGradCAM , LayerCAM, EigenGradCAM and EigenCAM.

  • The line images = model( img_path="/location/image.jpg" ) passes the images the model will process

  • The line display_images(images) displays the output of the model, along with the CAM model's output.

You can add a single image or a directory images to be used by the Module. The output will be a corresponding list of images (list containing one PIL Image for a single image input and list containing as many PIL images as Images in the input directory).