YOLOv8_Explainer

Simplify your understanding of YOLOv8 Results

This is a package with state of the art 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=[10, 12, 14, 16, 18, -3],
        backward_type="all",
        ratio=0.02,
        show_box=True,
        renormalize=False,
)

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

display_images(imagelist)

You can choose between the following CAM Models for version 0.0.2:

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

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 contianing one PIL Image for a single image imput and list contining as many PIL images as Images in the input directory).

References

https://github.com/jacobgil/pytorch-grad-cam
PyTorch library for CAM methods Jacob Gildenblat and contributors

https://github.com/z1069614715/objectdetection_script
Object Detection Script Devil's Mask

https://arxiv.org/abs/1610.02391
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, Dhruv Batra

https://arxiv.org/abs/2011.08891
Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks Rachel L. Draelos, Lawrence Carin

https://arxiv.org/abs/1710.11063
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks Aditya Chattopadhyay, Anirban Sarkar, Prantik Howlader, Vineeth N Balasubramanian

https://arxiv.org/abs/1910.01279
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks Haofan Wang, Zifan Wang, Mengnan Du, Fan Yang, Zijian Zhang, Sirui Ding, Piotr Mardziel, Xia Hu

https://ieeexplore.ieee.org/abstract/document/9093360/
Ablation-cam: Visual explanations for deep convolutional network via gradient-free localization. Saurabh Desai and Harish G Ramaswamy. In WACV, pages 972–980, 2020

https://arxiv.org/abs/2008.02312
Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs Ruigang Fu, Qingyong Hu, Xiaohu Dong, Yulan Guo, Yinghui Gao, Biao Li

https://arxiv.org/abs/2008.00299
Eigen-CAM: Class Activation Map using Principal Components Mohammed Bany Muhammad, Mohammed Yeasin

http://mftp.mmcheng.net/Papers/21TIP_LayerCAM.pdf
LayerCAM: Exploring Hierarchical Class Activation Maps for Localization Peng-Tao Jiang; Chang-Bin Zhang; Qibin Hou; Ming-Ming Cheng; Yunchao Wei

https://arxiv.org/abs/1905.00780
Full-Gradient Representation for Neural Network Visualization \n Suraj Srinivas, Francois Fleuret

https://arxiv.org/abs/1806.10206
Deep Feature Factorization For Concept Discovery Edo Collins, Radhakrishna Achanta, Sabine Süsstrunk