edgenext_xx_small 这个模型权重20M 本文从减轻计算资源消耗以部署于边缘设备入手,提出EdgeNeXt混合架构,该架构引入分割深度转置注意力(SDTA)编码器,通过通道组分割与合理的注意力机制来提高资源利用率。该架构凭借1.3M的参数量在ImageNet-1K上表现超过MobileViT,扩容后凭借5.6M的参数量在ImageNet-1K上达到79.4%的最高...
edgenext_small_usi 81.07 5.59M 1.26G model edgenext_small 79.41 5.59M 1.26G model edgenext_x_small 74.96 2.34M 538M model edgenext_xx_small 71.23 1.33M 261M model edgenext_small_bn_hs 78.39 5.58M 1.25G model edgenext_x_small_bn_hs 74.87 2.34M 536M model edgenext_xx_small_bn_hs ...
edgenext_small_ascend.yaml edgenext_x_small_ascend.yaml edgenext_xx_small_ascend.yaml efficientnet ghostnet googlenet halonet hrnet inceptionv3 inceptionv4 mixnet mnasnet mobilenetv1 mobilenetv2 mobilenetv3 mobilevit nasnet pit poolformer pvt pvtv2 regnet repmlp repvgg res2net resnest resnet resnet...
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mm = nat.DiNAT_Small(pretrained=True) model_surgery.export_onnx(mm, fuse_conv_bn=True, batch_size=1, simplify=True)# Exported simplified onnx: dinat_small.onnx# Run testfromkeras_cv_attention_models.imagenetimporteval_func aa = eval_func.ONNXModelInterf(mm.name +'.onnx') ...
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EdgeNeXt_XX_Small 1.33M 266M 256 71.23 902.957 qps EdgeNeXt_X_Small 2.34M 547M 256 74.96 638.346 qps EdgeNeXt_Small 5.59M 1.27G 256 79.41 536.762 qps - usi 5.59M 1.27G 256 81.07 536.762 qps EdgeNeXt_Base 18.5M 3.86G 256 82.47 383.461 qps - usi 18.5M 3.86G 256 83.31 383.461 qps ...
janpio/mre-pg-nextjs-edgePublic forked fromandyjy/mre-pg-nextjs-edge NotificationsYou must be signed in to change notification settings Fork0 Star0 Breadcrumbs mre-pg-nextjs-edge / Latest commit andyjy add pg Jun 6, 2024 dea17fd·Jun 6, 2024 ...
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For a long time in the past, large convolution kernels were completely abandoned, because multiple layers of small convolution kernels can achieve the same receptive field while having fewer parameters, resulting in better model performance. However, in Swin Transformer, large convolution kernels are ...