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README.md

@@ -1,5 +1,5 @@
 # Image_manipulation_detection
-Paper: CVPR2018, [Learning Rich Features for Image Manipulation Detection](/Learning Rich Features for Image Manipulation Dection.pdf)  
+Paper: CVPR2018, [Learning Rich Features for Image Manipulation Detection](https://arxiv.org/pdf/1805.04953.pdf)  
 Code based on [Faster-RCNN](https://github.com/dBeker/Faster-RCNN-TensorFlow-Python3.5)  
 
 This is a rough implementation of the paper. Since I do not have a titan gpu, I made some modifications on the algorithm, but you can easily change them back if you want the exact setting from the paper.
@@ -9,19 +9,22 @@ This is a rough implementation of the paper. Since I do not have a titan gpu, I
 Python 3.6
 TensorFlow 1.8.0
 
-# Note
-The code requires a large memory GPU. If you do not have a 6G+ GPU, please reduce the number of noise stream conv layers for training.
-
 # Setup
 - Download vgg16 pre-trained weights from [here](http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz)
+    - save to /data/imagenet_weights/vgg16.ckpt
 - Two-stream neural network model: [lib/nets/vgg16.py](lib/nets/vgg16.py)
     - noise stream's weights are randomly initialized
     - for accurate prediction, please pre-train noise stream's vgg weights on `ImageNet` and overwrite the trainable setting of noise stream after `SRM` conv layer
 - Bounding boxes are predicted by both streams.
     - In the paper, `RGB stream` alone predicts bbox more accurately, so you may wanna change that as well (also defined in vgg16.py)
 - Use `main_create_training_set.py` to create training set from `PASCAL VOC` dataset.
     - The generated dataset will follow the `pascal voc` style, which is also required by `train.py`
-
+- `Tensorboard` file will be save at `/default`
+- Weights will be save to `/default/DIY_detaset/default`
+
+# Note
+The code requires a large memory GPU. If you do not have a 6G+ GPU, please reduce the number of noise stream conv layers for training.
+
 # Demo results
 Dataset size: 10000, epoch: 3
 ![](_image/results.png)