将低分辨率图像——>高分辨率图像 Deep Learning for Image Super-resolution: A Survey 基于插值的技术 最近邻元法 双线性内插法 三次内插法 基于重建的方法 概率论/集合论 凸集投影法(POCS) 贝叶斯分析方法 迭代反投影法(IBP) 后验概率方法 正规化法 混合方法 基于机器学习的方法(非深度学习) Example-based...
segmentation = TF.rotate(segmentation, angle) # more transforms ... return image, segmentation 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. ref to official api :https://pytorch.org/docs/stable/主要有一下几类: 1)针对PIL image的处理---> 主要包括一些图像的裁剪、翻转、亮度变化等增强 2)...
docker镜像 [neuware_sdk]/PyTorch/docker/pytorch-0.15.604-ubuntu18.04.tar.gz 包含catch的example,将PyTorch和Catch编译成wheel包以及Python 3虚拟环境,且已经安装了cntoolkit等依赖包。 tree /torch/ -L 1 ├── examples -- 在线与离线demo ├── requirements.txt -- Python依赖包 ├── src -- PyTorc...
After you build the container image, you can start an interactive CLI session with bash scripts/docker/interactive.sh <path/to/dataset/> The interactive.sh script requires that the location on the dataset is specified. For example, /home/<USER>/Detectron_PyT/detectron/lib/datasets/data/coco ...
For example, At groups=1, all inputs are convolved to all outputs. At groups=2, the operation becomes equivalent to having two conv layers side by side, each seeing half the input channels, and producing half the output channels, and both subsequently concatenated. At groups= in_channels, ...
See example at https://huggingface.co/gaunernst/vit_base_patch16_1024_128.audiomae_as2m_ft_as20k/blob/main/config.json Usage: #2035 Updated imagenet eval and test set csv files with latest models vision_transformer.py typing and doc cleanup by Laureηt 0.9.11 release Nov 3, 2023 DFN ...
│ ├── detection_example.png │ ├── detection_examples.png │ ├── SSD.jpg │ └── ssd.png ├── eval.py ├── layers │ ├── box_utils.py │ ├── functions │ │ ├── detection.py │ │ ├── __init__.py ...
classMyMainModel(LightningModule):def__init__(self,encoder:nn.Module,decoder:nn.Module):"""Example encoder-decoder submodules modelArgs:encoder: Instance of a module for encodingdecoder: Instance of a module for decoding"""super().__init__()self.encoder=encoderself.decoder=decoder ...
1. Create your first Segmentation model with SMP Segmentation model is just a PyTorch nn.Module, which can be created as easy as: importsegmentation_models_pytorchassmp model = smp.Unet( encoder_name="resnet34",# choose encoder, e.g. mobilenet_v2 or efficientnet-b7encoder_weights="imagenet...
image,input;t_video_in>>image;cv::cvtColor(image,input,CV_BGR2RGB);// run semantic segmentation to get label imagetorch::Tensor tensor_image=torch::from_blob(input.data,{1,input.rows,input.cols,3},torch::kByte);tensor_image=tensor_image.permute({0,3,1,2});tensor_image=tensor_image...