预告性文本模型权重.zip

PyTorch 模型部署到 ONNX，实现一个超分辨率模型，并把模型部署到 ONNX Runtime 这个推理引擎上。

云模型matlab代码，可以直接使用

TransUet官方代码中需要的预训练权重 这个是最小的一个模型权重，需要其他权重的可以私信我。

包含4个权重文件，yolov5l ，yolov5m , yolov5s, yolov5x 。 从谷歌云盘下载的，4个文件4个积分不多吧.

Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

fall-detect-track项目的模型权重

caffe常用网络模型权重文件和定义文件（alex, vgg, googlenet, resnet）-附件资源

darknet是一个较为轻型的完全基于C与CUDA的开源深度学习框架，其主要特点就是容易安装，没有任何依赖项（OpenCV都可以不用），移植性非常好，支持CPU与GPU两种计算方式。Darknet的优势： darknet完全由C语言实现，没有任何依赖项，当然可以使用OpenCV，但只是用其来显示图片、为了更好的可视化； darknet支持CPU（所以没有GPU也不用紧的）与GPU（CUDA/cuDNN，使用GPU当然更块更好了）； 正是因为其较为轻型，没有像TensorFlow那般强大的API，所以给我的感觉就是有另一种味道的灵活性，适合用来研究底层，可以更为方便的从底层对其进行改进与扩展

适用于pytorch的网络模型ResNet的模型权重 resnet101-5d3b4d8f.pth resnet152-b121ed2d.pth resnet18-5c106cde.pth resnet34-333f7ec4.pth resnet50-19c8e357.pth resnext101_32x8d-8ba56ff5.pth resnext50_32x4d-7cdf4587.pth 调用方法： model = models.resnet50(pretrained=False) model.load_state_dict(torch.load('weight/resnet50/resnet50-19c8e357.pth')) model.fc = nn.Linear(model.fc.in_features, CLASS_NUM) pretrained表示不下载权重，若用于图片分类，可以在后面加多一层，用来输出CLASS_NUM个结果，即CLASS_NUM个类别