1.启动MindStudio。 2.本文以SE-ResNeXt101模型为例,介绍使用MindStudio进行PyTorch模型训练开发。 下载SE-ResNeXt101项目代码,项目地址为: https://gitee.com/ascend/modelzoo-his/tree/master/contrib/PyTorch/Research/cv/image_classification/SE-ResNext-101-32x4d 3.新建Ascend Training工程。工程信息由用户自行根...
数据集 SE_ResNeXt101_32x4d_pretrained 拷贝自 paddle 的 github,裁剪掉最后一个全连接层 文件列表 SE_ResNeXt101P.zip SE_ResNeXt101P.zip (169.35M) 下载 File Name Size Update Time SE_ResNeXt101P/conv1_bn_mean 280 2018-12-23 16:56:02 SE_ResNeXt101P/conv1_bn_offset 280 2018-12-23 16:56...
se-resnext101表现不凡,时间上大约比x101多了0.5h(x101 4h),在我数据集上0.763(x101 0.754),senet154时间7h, 但精度高达76.9,而且两者都是没有coco参数的情况下(x101用了coco),如果有了coco参数可能要好一点,如果有同学对源码有兴趣欢迎留言,有点懒得整理 ...
A collection of pre-trained, state-of-the-art models in the ONNX format - onnx-models/Computer_Vision/gluon_seresnext101_64x4d_Opset16_timm at main · xiaozhiob/onnx-models
se_resnext101_32x4d.pth(196.47 MB) get_app fullscreen chevron_right Unable to show preview Previews for binary data are not supported Data Explorer Version 1 (196.47 MB) insert_drive_file se_resnext101_32x4d.pth Summary arrow_right folder 1 file lightbulb See what others are saying about th...
2,上述通过AC结构和SE结构添加了空间注意力机制和通道注意力机制的AC-SE-ResNeXt网络模型,可实现端到端的人脸关键点检测,在单阶段只使用一个网络的情况下,不仅避免了级联策略中多阶段回归的算法流程复杂性,还解决了相邻阶段间数据需要进行预处理的问题. 3,对训练好的模型分别在数据集BioID和LFPW上进行测试,其中在...
* 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。 README.md ImageNet Pre-trained Weights for SE-ResNeXt50 and 101 These are converted for ChainerCV. Orignal weights are distributed at GitHub: https://github.com/hujie-frank/SENet Paper: [Jie Hu, Li Shen and Gang Sun. **Squeeze-and...
2.本文以SE-ResNeXt101模型为例,介绍使用MindStudio进行PyTorch模型训练开发。 下载SE-ResNeXt101项目代码,项目地址为: https://gitee.com/ascend/modelzoo-his/tree/master/contrib/PyTorch/Research/cv/image_classification/SE-ResNext-101-32x4d 3.新建Ascend Training工程。工程信息由用户自行根据实际配置。单击“Nex...
A collection of pre-trained, state-of-the-art models in the ONNX format - onnx-models/Computer_Vision/gluon_seresnext101_64x4d_Opset17_timm at main · xiaozhiob/onnx-models
A collection of pre-trained, state-of-the-art models in the ONNX format - onnx-models/Computer_Vision/gluon_seresnext101_32x4d_Opset16_timm at main · xiaozhiob/onnx-models