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): ...
rigvedrs/YOLO-V11-CAM Star189 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 ...
cam=EigenCAM(model,target_layers,task='cls')grayscale_cam=cam(rgb_img)[0, :, :]cam_image=show_cam_on_image(img,grayscale_cam,use_rgb=True)plt.imshow(cam_image)plt.show() For the Object Detection Task, just change the task to 'od' and the rest is same. ...
No Class Discrimination: Unlike Grad-CAM, which associates regions with specific classes, EigenCAM simply identifies the most significant features. This method provides a broad understanding of feature importance but does not differentiate between categories. Gradient-Free Activation Analysis: EigenCAM uses...
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 ...
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)...
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 ...
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): ...