"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-...
Outputmore_vert insert_drive_file inference.log calendar_view_week submission.csv Download notebook output navigate_nextminimize content_copyhelp
check_circle Successfully ran in 51.4s Accelerator GPU P100 Environment Latest Container Image Output 32 B Time # Log Message 29.1s 1 31.1s 2 32.9s 3 34.6s 4 36.5s 5 38.0s 6 47.5s 7 49.7s 8 [NbConvertApp] Converting notebook __notebook__.ipynb to notebook 50.0s 9 [Nb...
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...
Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals.
Explore and run machine learning code with Kaggle Notebooks | Using data from se_resnext50_32x4d_080922