Update README.md

README.md

@@ -27,7 +27,7 @@ The TF operators are included under `tf_ops`, you need to compile them first by
 
 ## Gesture Recognition on SHREC'17 without Bounding Boxes
 ### Data Preparation
-We provide the point cloud data of ***SHREC'17 without Bounding Boxes*** [here](), which (`BackgroundSHREC2017`) should be downloaded to `./dataset/SHREC2017`. Following previous researches on the same dataset with bounding boxes, we generate 256 points per frame and sample 128 for training a 32-frame classifier. The data splitting files can be found as follows - download the [SHREC'17 dataset](http://www-rech.telecom-lille.fr/shrec2017-hand/) and put `HandGestureDataset_SHREC2017` directory to `./dataset/SHREC2017`, It is suggested to make a soft link toward the downloaded dataset.
+We provide the point cloud data of ***SHREC'17 without Bounding Boxes*** [here](BackgroundSHREC2017.7z), which (`BackgroundSHREC2017`) should be downloaded to `./dataset/SHREC2017`. Following previous researches on the same dataset with bounding boxes, we generate 256 points per frame and sample 128 for training a 32-frame classifier. The data splitting files can be found as follows - download the [SHREC'17 dataset](http://www-rech.telecom-lille.fr/shrec2017-hand/) and put `HandGestureDataset_SHREC2017` directory to `./dataset/SHREC2017`, It is suggested to make a soft link toward the downloaded dataset.
 ### Train
 First, let's train the static branch (For better performance, pretraining on additional static datasets like *ModelNet40* is probably helpful):
 ```
@@ -39,7 +39,7 @@ Then, train the dynamic branch:
 bash train_dynamic.sh
 ```
 ### Test a Trained Model
-A trained model is provided [here](). The performance of static, dynamic & fusion branches can be evaluated:
+A trained model is provided [here](trained_dynamic_model.zip). The performance of static, dynamic & fusion branches can be evaluated:
 ```
 bash test.sh
 ```