update readme

README.md

@@ -1,2 +1,32 @@
 # mxnet-E2FAR
-MXNET/Gluon Implementation of End-to-end 3D Face Reconstruction with Deep Neural Networks
+This is a MXNet/Gluon Implementation of End-to-end 3D Face Reconstruction with Deep Neural Networks.
+
+1. Download VGG-Face and convert it to the mxnet-weights by running the caffe_converter:
+```
+python $MXNET/tools/caffe_converter/convert_model.py prototxt weights params_name
+```
+Put the weights into the folder ```ckpt/VGG-Face```
+
+2. Prepare the dataset
+
+3. For train your dataset, you may need to change the ```dataset``` in the main code to fit your dataset
+
+Run the code:
+```
+# fine-tune the branch and fully connected layers
+python E2FAR.py --pretrained --freeze --epoch 10
+
+# fine-tune whole network
+python E2FAR.py --start_epoch 10
+```
+
+If you use this code, pls mention this repo and cite the paper:
+```
+@InProceedings{Dou_2017_CVPR,
+author = {Dou, Pengfei and Shah, Shishir K. and Kakadiaris, Ioannis A.},
+title = {End-To-End 3D Face Reconstruction With Deep Neural Networks},
+booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+month = {July},
+year = {2017}
+}
+```