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diff --git a/README.md b/README.md
index 1b9d17d..33fc36b 100644
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@@ -36,7 +36,7 @@ cd Data
 ```
 
 ## 3. Testing on saved models
-* Download the trained models
+* Download the trained models (it contains the pretrained FlowNet and the trained models of papers, such as ped1, ped2 and avenue).
 ```shell
 cd checkpoints
 ./download_pretrains.sh
@@ -58,6 +58,7 @@ python inference.py  --dataset  avenue    \
 
 
 ## 4. Training from scratch (here we use ped2 and avenue datasets for examples)
+* Download the pretrained FlowNet at first and see above mentioned step 3.1 
 * Set hyper-parameters
 The default hyper-parameters, such as $\lambda_{init}$, $\lambda_{gd}$, $\lambda_{op}$, $\lambda_{adv}$ and the learning rate of G, as well as D, are all initialized in **training_hyper_params/hyper_params.ini**. 
 * Running script (as ped2 or avenue for instances) and cd into **Codes** folder at first.