"runs/" + "May29_06-04-06_cs231n-1se_resnext50_32x4d-bs-64-clr0.006-0.0006-mom0.9-wd1e-5-scale0.3-0.6-rota15-cas-best-classes25-trainval" + "/model_best.pth.tar", # 0.6631, PW1 "runs/" + "May29_19-25-32_cs231n-1se_resnext101_32x4d-bs-64-lr0.006-mom0.9-wd1e-5...
def get_se_resnext50_32x4d_model(model_class, num_classes, model_file=None, pretrained=False): model = model_class(num_classes=1000, pretrained='imagenet' if pretrained else False) model = model_class(num_classes=1000, pretrained='imagenet' if pretrained else None) num_ftrs = model....
ResNet,作者选择了很简单的基本结构,每一组C个不同的分支都进行相同的简单变换,下面是ResNeXt-50(32x4d)的配置清单,32指进入网络的第一个ResNeXt基本结构的分组数量C(即基数)为32,4d表示depth即每一个分组的通道数为4(所以第一个基本结构输入通道数为128): 可以看到ResNet-50和ResNeXt-50(32x4d)拥有相同的参...
东南resnext50 32x4d 105.44M 407浏览 0 0次下载 0条讨论 OthersClassification Share Favorite 0 0 数据介绍 文件预览 相关论文 Code 分享讨论(0) 使用声明 启动Notebook开发 数据结构?105.44M * 以上分析是由系统提取分析形成的结果,具体实际数据为准。
"runs/" + "May23_21-11-42_cs231n-1se_resnext50_32x4d-bs-64-clr0.06-0.006-mom0.9-wd1e-5-cutout4-minscale0.4-rota15-cas" + "/model_best.pth.tar", # 0.655, PW1 "runs/" + "May24_07-07-00_cs231n-1se_resnext50_32x4d-bs-64-lr0.0006-mom0.9-wd1e-5-cutout4-minscale0.4-...
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cassava-resnext50-32x4d-weights moa-b4-baseline efficientnet-pytorch-07 trending_flat See All NOTEBOOKS Plant Pathology: Pytorch efficientnet_b4 GPU Cassava / resnext50_32x4d starter [training] Pytorch Efficientnet Baseline [Train] AMP+Aug Fork Pytorch Efficientnet Baseline [Train] AMP+A Tags GPU La...
Learn more OK, Got it. Something went wrong and this page crashed! If the issue persists, it's likely a problem on our side. Unexpected end of JSON inputkeyboard_arrow_upcontent_copySyntaxError: Unexpected end of JSON inputRefresh