资源来自：https://github.com/1990571096/SRCNN-Pytorch

# SRCNN_Pytorch CSDN博客代码讲解地址：https://blog.csdn.net/weixin_52261094/article/details/128389448 ## Requirements - PyTorch 1.0.0 - Numpy 1.15.4 - Pillow 5.4.1 - h5py 2.8.0 - tqdm 4.30.0 ### Step1: 训练集，测速集下载地址， #### 也可以用 preapre自己制作训练集和测试集 | Dataset | Scale | Type | Link | |---------|-------|------|------| | 91-image | 2 | Train | [Download](https://github.com/learner-lu/image-super-resolution/releases/download/v0.0.1/91-image_x2.h5) | | 91-image | 3 | Train | [Download](https://github.com/learner-lu/image-super-resolution/releases/download/v0.0.1/91-image_x3.h5) | | 91-image | 4 | Train | [Download](https://github.com/learner-lu/image-super-resolution/releases/download/v0.0.1/91-image_x4.h5) | | Set5 | 2 | Eval | [Download](https://github.com/learner-lu/image-super-resolution/releases/download/v0.0.1/Set5_x2.h5) | | Set5 | 3 | Eval | [Download](https://github.com/learner-lu/image-super-resolution/releases/download/v0.0.1/Set5_x3.h5) | | Set5 | 4 | Eval | [Download](https://github.com/learner-lu/image-super-resolution/releases/download/v0.0.1/Set5_x4.h5) | Download any one of 91-image and Set5 in the same Scale and then **move them under `./datasets` as `./datasets/91-image_x2.h5` and `./datasets/Set5_x2.h5`** ### Step2: 训练模型 --train-file "path_to_train_file" \ --eval-file "path_to_eval_file" \ --outputs-dir "path_to_outputs_file" \ --scale 3 \ --lr 1e-4 \ --batch-size 16 \ --num-epochs 400 \ --num-workers 0 \ --seed 123 ### Step3: 400轮训练结果，训练得到的最优权重 - [trained by x3](https://pan.baidu.com/s/1sLGsDPuC7BCUaVMDRv013A?pwd=1234) **将权重移动到该目录文件下： `./outputs` as `./outputs/x3/best.pth`**