为了提高内存效率并增强通道间的通信,EfficientViT设计了一种新的构建块,采用了“夹层布局(sandwich layout)”策略,即在高效的前馈神经网络FFN层之间使用了一个受内存限制的MHSA。 EfficientViT以EfficientViT block作为基础模块,每个模块由夹层结构(Sandwich Layout)和级联组注意力(Cascaded Group Attention, CGA)组成,进...
efficientdet,edgenext,efficientformer,efficientnet,eva,fasternet,fastervit,fastvit,flexivit,gcvit,ghostnet,gpvit,hornet,hiera,iformer,inceptionnext,lcnet,levit,maxvit,mobilevit,moganet,nat,nfnets,pvt,swin,tinynet,tinyvit,uniformer,volo,vanillanet,yolor,yolov7,yolov8,yolox,gpt2,llama2, alias ...
[Deprecated]tensorflow_addonsis not imported by default. While reloading model depending onGroupNormalizationlikeMobileViTV2fromh5directly, needs to importtensorflow_addonsmanually first. importtensorflow_addonsastfa model_path = os.path.expanduser('~/.keras/models/mobilevit_v2_050_256_imagenet.h5')...
通过全面的实验证明了EfficientViT在速度和准确性之间取得了良好的平衡,并超越了现有的高效模型。 将EfficientViT引入到YoloV7中,打造实时高效的YoloV7,效果如何呢?这篇文章将告诉你答案! YoloV7 官方代码测试结果 all 229 1407 0.966 0.99 0.993 0.734 c17 229 131 0.977 0.992 0.991 0.828 c5 229 68 0.941 1...
CUDA_VISIBLE_DEVICES='1' ./coco_eval_script.py -m yolox.YOLOXTiny --use_bgr_input --nms_method hard --nms_iou_or_sigma 0.65 # >>> [COCOEvalCallback] input_shape: (416, 416), pyramid_levels: [3, 5], anchors_mode: anchor_free # YOLOR / YOLOV7 using letterbox_pad and other...