update README.md

Author: hunto

README.md

@@ -27,5 +27,45 @@ The core concept of DyRep is in `lib/models/utils/dyrep.py`.
     ```
 * CIFAR-100
     ```
-    sh tools/dist_train.sh 1 configs/strategies/DyRep/cifar.yaml nas_model --model-config configs/models/VGG/vgg16_cifar10.yaml --dyrep --experiment dyrep_cifar10_vgg16
-    ```
+    sh tools/dist_train.sh 1 configs/strategies/DyRep/cifar.yaml nas_model --model-config configs/models/VGG/vgg16_cifar100.yaml --dyrep --dataset cifar100 --experiment dyrep_cifar100_vgg16
+    ```
+
+### ImageNet  
+* ResNets  
+    ```
+    sh tools/dist_train.sh 8 configs/strategies/DyRep/resnet.yaml resnet50 --dyrep --experiment dyrep_imagenet_res50
+    ```
+
+* MobileNetV1
+    ```
+    sh tools/dist_train.sh 8 configs/strategies/DyRep/mbv1.yaml mobilenet_v1 --dyrep --experiment dyrep_imagenet_mbv1
+    ```
+
+* RepVGG
+    * DyRep-A2
+        ```
+        sh tools/dist_train.sh 8 configs/strategies/DyRep/repvgg_baseline.yaml timm_repvgg_a2 --dyrep --dyrep_recal_bn_every_epoch --experiment dyrep_imagenet_repvgg_a2
+        ```
+    * DyRep-B2g4 and DyRep-B3
+        ```
+        sh tools/dist_train.sh 8 configs/strategies/DyRep/repvgg_strong.yaml timm_repvgg_b2g4 --dyrep --dyrep_recal_bn_every_epoch --experiment dyrep_imagenet_repvgg_b2g4
+        ```
+
+## Deploying the Trained DyRep Models to Inference Models  
+```
+sh tools/dist_convert.sh 8 ${CONFIG} ${MODEL} --resume ${CHECKPOINT}
+```
+
+For example, if you want to deploy the trained ResNet-50 model with the best checkpoint, run  
+```
+sh tools/dist_convert.sh 8 configs/strategies/DyRep/resnet.yaml resnet50 --dyrep --resume experiments/dyrep_imagenet_res50/best.pth.tar
+```
+
+Then it will run test before and after deployment to ensure the accuracy will not drop.
+
+The final weights of the inference model will be saved in `experiments/dyrep_imagenet_res50/convert/model.ckpt`.
+
+## Results  
+
+## Citation  
+The paper will be released soon.