我们使用Grad-CAM[48]来可视化在ImageNet-1K上训练的不同模型的结果。我们发现,虽然ResMLP[52]也激活了一些不相关的部分,但所有模型都能定位到语义对象。在图中,DeiT[53]和ResMLP[52]的激活部分更为分散,而RSB-ResNet[24,59]和PoolFormer的激活部分更为集中。 D. Layer Normalization与Modified Layer Normalizatio...
通过将注意力模块视为一个特定的令牌混合器,我们进一步将整体的Transformer抽象为一个一般架构MetaFormer,其中不指定令牌混合器,如图1(a)所示。 长期以来,Transformer的成功一直归因于基于注意力的令牌混合器[56]。基于这一普遍信念,开发了许多注意力模块的变体[13,22,57,68]来改进Vision Transformer。然而,最近的一项...
To delve deeper into the impact of CRNet and gain a clearer understanding of its role in detecting strawberry ripeness, the Grad-CAM heat map visualization technique emphasizes the model’s ability to identify the maturity level of strawberries by analyzing the weight of the ‘maturity’ category...
We also used Grad-CAM to intuitively illustrate the feature extraction results of YOLOv9 and YOLO-IAPs. Figure 11 presents examples of predictions made by YOLOv9 and YOLO-IAPs on images of five IAPs species. While both models produced visually satisfactory predictions, a comparison reveals that ...
在这里,webcam变量会根据--source参数的值被设置为True,source变量会包含0,表示使用默认的摄像头。 进行目标检测: 加载完模型和设置好视频源后,detect.py会开始捕获视频帧,并对每一帧进行目标检测。这通常是通过模型的前向传播来实现的: python with torch.no_grad(): pred = model(img, augment=augment)[0...