检测模型基于Tiny YOLO v3架构,对可重新配置的硬件进行了推理优化,并包含两个检测头,以说明具有不同规模的目标。每个头部的最后一个卷积层存储每个潜在边界框的多个分数:(i)objectness,它提供了一般观察目标的可能性;(ii)所有目标类的类概率向量。对于头部1,该层的大小为1x1x512x30,对于头部2,该层为1x1x256x30。
pytorchgradcamyolov3 UpdatedMay 15, 2020 Python da2so/GradCAM_PyTorch Star27 Code Issues Pull requests GradCAM Pytorch explainable-aigradcam UpdatedAug 30, 2024 Python stavrostheocharis/easy_explain Star12 Code Issues Pull requests An XAI library that helps to explain AI models in a really quick...
[重读经典论文]YOLOv42023-05-0628.[重读经典论文]YOLOv32023-04-2829.[经典论文重读]YOLOv22023-04-2830.[重读经典论文]YOLOv12023-04-2631.[重读经典论文] MobileViT2023-06-1332.[重读经典论文] ConvNeXt——卷积网络又行了2023-06-1233.[重读经典论文] Swin-Transformer2023-06-1134.[重读经典论文]VIT...
## 3. 目前支持基于FasterRCNN和YOLOv3系列的网络。 * FasterRCNN网络热图可视化脚本 ```bash python tools/cam_ppdet.py -c configs/faster_rcnn/faster_rcnn_r50_vd_fpn_2x_coco.yml --infer_img demo/000000014439.jpg --cam_out cam_faster_rcnn -o weights=https://paddledet.bj.bcebos.com/model...
不需要对源码作任何修改的yolov8热力图可视化。支持类别和box的反向传播求梯度!不需要对源码做任何修改,直接下载即插即用! github地址:https://github.com/z1069614715/objectdetection_script/tree/master/yolo-gradcam 博客地址:https://blog.csdn.net/qq_37706472/article/details/128714604?spm=1001.2014.3001.5501 ...
YOLOv3目标检测:原理与源码解析 4.9白老师 ￥78.00 YOLOv3目标检测实战:训练自己的数据集 4.8白老师 ￥58.00 Mask R-CNN图像实例分割实战:训练自己的数据集 5.0白老师 ￥88.00 UNet(TensorFolow2)图像语义分割实战:训练自己的数据集 5.0白老师 ￥88.00 PyTorch版Mask R-CNN图像实例分割实战:训练自己的数据集(Detectr...
First, a YOLO CNN with pre-trained weights on ImageNet is retrained with manually cropped elephant ears to localise them in the colour image. Second, these cropped ear patches are learnt by a CNN to classify each elephant by the Zoologist's labelling; Xception outperformed VGG16, ResNet50, ...
目前支持的算法包括YOLO V3，Faster R-CNN和RetinaNet的可视化，如图：Yolo V3：Faster R-CNN：FRCN ...
目前支持的算法包括YOLO V3，Faster R-CNN和RetinaNet的可视化，如图：Yolo V3：Faster R-CNN：FRCN ...
[11] used the deep learning-based object detection method You Only Look Once (YOLOv3)to find microalgae in an experiment. The model was trained using microscopic images, and darknet-53 served as the framework. This study also showed that a model using color images performs better during ...