Update: Add training demo.

README.md

@@ -51,6 +51,59 @@ We named this project IDDM: Industrial Defect Diffusion Model. It aims to reprod
 
 ### Training
 
+#### Start Your First Training (Using cifar10 as an Example, Single GPU Mode)
+
+1. **Import the Dataset**
+
+   First, upload the dataset to the target folder `datasets`. After uploading, the folder structure (for example, under the `cifar10` folder, there are folders for each class; `class0` folder contains all images for class 0) should look like the following:
+
+   ```yaml
+    datasets
+    └── cifar10
+        ├── class0
+        ├── class1
+        ├── class2
+        ├── class3
+        ├── class4
+        ├── class5
+        ├── class6
+        ├── class7
+        ├── class8
+        └── class9
+   ```
+
+   At this point, your pre-training setup is complete.
+
+2. **Set Training Parameters**
+
+   Open the `train.py` file and modify the `parser` parameters inside the `if __name__ == "__main__":` block.
+
+   Set the `--conditional` parameter to `True` because it's a multi-class training, so this needs to be enabled. For single-class, you can either not enable it or enable it.
+
+   Set the `--run_name` parameter to the desired file name you want to create, for example, `cifar_exp1`.
+
+   Set the `--dataset_path` parameter to the file path on your local or remote server, such as `/your/local/or/remote/server/file/path/datasets/cifar10`.
+
+   Set the `--result_path` parameter to the file path on your local or remote server where you want to save the results.
+
+   Set the `--num_classes` parameter to `10` (this is the total number of your classes.
+
+   Set any other custom parameters as needed. If the error `CUDA out of memory` is shown in your terminal, turn down `--batch_size` and `num_workers`.
+
+   For detailed commands, refer to the **Training Parameters** section.
+
+3. **Wait for the Training Process**
+
+   After clicking `run`, the project will create a `cifar_exp1` folder in the `results` folder. This folder will contain training log files, model training files, model EMA (Exponential Moving Average) files, model optimizer files, all files saved during the last training iteration, and generated images after evaluation.
+
+4. **View the Results**
+
+   You can find the training results in the `results/cifar_exp1` folder.
+
+
+
+**↓↓↓↓↓↓↓↓↓↓The following is an explanation of various training methods and detailed training parameters↓↓↓↓↓↓↓↓↓↓**
+
 #### Normal Training
 
 1. Take the `landscape` dataset as an example and place the dataset files in the `datasets` folder. The overall path of the dataset should be `/your/path/datasets/landscape`, and the image files should be located at `/your/path/datasets/landscape/*.jpg`.

README_zh.md

@@ -51,6 +51,58 @@
 
 ### 训练
 
+#### 开始你的第一个训练（以cifar10为例，模式单卡）
+
+1. **导入数据集** 
+
+   首先，将数据集上传至目标文件夹`datasets`中。上传后文件夹格式（例如：cifar10文件夹下存放着所有类别；class0文件夹下存储着class0这个类别的所有图片）如下方列表所示：
+   ```yaml
+    datasets
+    └── cifar10
+        ├── class0
+        ├── class1
+        ├── class2
+        ├── class3
+        ├── class4
+        ├── class5
+        ├── class6
+        ├── class7
+        ├── class8
+        └── class9
+   ```
+
+   此时你的训练前准备已经完毕。
+
+2. **设置训练参数**
+
+   打开`train.py`文件，修改`if __name__ == "__main__":`中的`parser`参数；
+
+   设置`--conditional`参数为`True`，因为是多类别训练，所以需要开启，单类别可以不开启也可以开启；
+
+   设置`--run_name`参数为你想创建的文件名称，例如`cifar_exp1`；
+
+   设置`--dataset_path`参数为`/你的/本地/或/远程服务器/文件/地址/datasets/cifar10`；
+
+   设置`--result_path`参数为`/你的/本地/或/远程服务器/文件/地址/results`；
+
+   设置`--num_classes`参数为`10`，这是你的类别总数；
+
+   设置更多参数（自定义），如果报`CUDA out of memory`错误，将`--batch_size`、`--num_workers`调小；
+
+   详细命令可参考**训练参数**。
+
+3. **等待训练过程**
+
+   点击`run`运行后，项目会在`results`文件夹中生成`cifar_exp1`文件夹，该文件夹中会保存训练日志文件、模型训练文件、模型EMA文件、模型优化器文件、训练的所有最后一次保存的文件和评估后生成的图片。
+
+4. **查看结果**
+
+   找到`results/cifar_exp1`文件夹即可查看训练结果。
+
+
+
+**↓↓↓↓↓↓↓↓↓↓下方为多种训练方式、训练详细参数讲解↓↓↓↓↓↓↓↓↓↓**
+
 #### 普通训练
 
 1. 以`landscape`数据集为例，将数据集文件放入`datasets`文件夹中，该数据集的总路径如下`/your/path/datasets/landscape`，数据集图片路径如下`/your/path/datasets/landscape/*.jpg`