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。
GradCAM algorithm implementation for YOLOv3 pytorchgradcamyolov3 UpdatedMay 15, 2020 Python da2so/GradCAM_PyTorch Star27 Code Issues Pull requests GradCAM Pytorch explainable-aigradcam UpdatedAug 30, 2024 Python stavrostheocharis/easy_explain
[轻量化网络] MobileNet V32023-06-0618.[重读经典论文] RetinaNet——Focal Loss for Dense Object Detection2023-06-0519.[重读经典论文] FPN及PAN笔记2023-06-0220.[重读经典论文]YOLOv72023-05-3021.[重读经典论文]YOLOV62023-05-3022.[重读经典论文]RepVGG: Making VGG-style ConvNets Great Again2023-...
# Todo: hard code for nms return index if cfg.architecture == 'FasterRCNN': trainer.model.bbox_post_process.nms.return_index = True elif cfg.architecture == 'YOLOv3': if trainer.model.post_process is not None: # anchor based YOLOs: YOLOv3,PP-YOLO trainer.model.post_process.nms.retu...
This paper presents a cascade of Convolutional Neural Networks for uniquely detecting Asian elephants with two steps: (1) an elephant-ear localisation step at a species level, and (2) an ear-patch classification step at an individual level. First, a YOLO CNN with pre-trained weights on Image...
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...
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...
The proposed technique surpasses You Only Look Once (YOLO) and SSD in terms of accuracy and IoU. Nie et al. [14] also designed the model for ship detection by using the Mask RCNN algorithm. In addition to that, a soft non maximum suppression method is implemented to enhance the ...