rigvedrs/YOLO-V11-CAM Star180 Code Issues Pull requests Wanna know what your model sees? Here's a package for applying EigenCAM and generating heatmap from the new YOLO V11 model computer-visionpytorchyolodeeplearninginterpretabilitygradcamclass-activation-mapyolov8eigencamyolov11 ...
frompytorch_grad_camimportGradCAMPlusPlus,GradCAM,XGradCAM frompytorch_grad_cam.utils.imageimportshow_cam_on_image frompytorch_grad_cam.activations_and_gradientsimportActivationsAndGradients defletterbox(im,new_shape=(640,640),color=(114,114,114),auto=True,scaleFill=False,scaleup=True,stride=32)...
EigenCAM is a technique that involves computing the first principle component of the 2D activations in a neural network, without taking class discrimination into account, and has been found to produce effective results. Image: GrayScale Heatmaps: ...
no_grad() def __init__(self, weights='yolov8n.pt', device=torch.device('cpu'), fp16=False, fuse=True, data=None): """ 初始化AutoBackend进行推理。 参数: weights (str): 模型权重文件的路径,默认为'yolov8n.pt'。 device (torch.device): 运行模型的设备,默认为CPU。 fp16 (bool): ...
im2col_step) return grad_input, grad_offset, grad_mask, None, None, None, None, None, None, None, None, None, None, None, None, None def dcnv3_core_pytorch(input, offset, mask, kernel_h, kernel_w, stride_h, stride_w, pad_h, pad_w, dilation_h, dilation_w, group, group_...
为了展示 YOLOv11 模型出色的特征提取能力,作者使用 Grad-CAM [18] 工具可视化了 YOLOv5、YOLOv8、YOLOv9、YOLOv10 和 YOLOv11 在识别电力设备目标时的检测结果。如图 4 所示,在电力设备目标检测区域的空间范围内,YOLOv11 模型表现出明显的注意力集中现象。与之相比,其前辈生成的注意力图分布更加分散,无法像 ...
from pytorch_grad_cam import GradCAMPlusPlus, GradCAM, XGradCAM from pytorch_grad_cam.utils.image import show_cam_on_image from pytorch_grad_cam.activations_and_gradients import ActivationsAndGradients def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill...
This article utilizes Gradient-Weighted Class Activation Mapping (Grad-CAM) [33] to generate a heatmap for the GCB-YOLOv11 model, aiming to analyze the model’s focus on various features. As illustrated inFigure 15, the heatmap is categorized into red, yellow, and blue, reflecting the orde...
Figure 11 shows the Grad-CAM-generated heat maps for YOLOv11n and LAMS-YOLO on the drone dataset. LAMS-YOLO demonstrates a superior focus on target regions. It also reduces attention to irrelevant background areas and non-target regions compared to YOLOv11n. Additionally, for targets of ...
Figure 11 shows the Grad-CAM-generated heat maps for YOLOv11n and LAMS-YOLO on the drone dataset. LAMS-YOLO demonstrates a superior focus on target regions. It also reduces attention to irrelevant background areas and non-target regions compared to YOLOv11n. Additionally, for targets of ...