add eval readme

evaluation/README.md

@@ -0,0 +1,20 @@
+Fawkes Evaluation
+-----------------
+
+
+We offer two ways to test the protection is effective, 1) train a local face recognition model using transfer learning, 2) use Microsoft Azure API. 
+Note that we can't guarantee the protection is always successful due to new development in face recognition technique. 
+
+Evaluation with Local Model
+---------------------------
+To evaluate using local model, you are highly recommended to have a GPU device and train the model on it. Otherwise, the evaluation will be extremely slow and might even damage the CPUs on some machine. 
+
+To evaluate, run `python3 eval_local.py -d IMAGE_DIR`. Where `IMAGE_DIR` is the image directory send to Fawkes protection code, and it must contain both original images (testing) and cloaked image (training). 
+All images in the directory must belong to the same person and have at least 10 images in them. Also, you cannot turn on `--seperate-target` during the protection. (We are working on reducing some of these limitations.)
+The script will output the protection success rate at the end. 
+
+
+Evaluation with Microsoft Azure
+---------------------------
+forthcoming...
+

fawkes/protection.py

@@ -113,8 +113,6 @@ class Fawkes(object):
 
         image_paths, loaded_images = filter_image_paths(image_paths)
 
-        start_time = time.time()
-
         if not image_paths:
             raise Exception("No images in the directory")
         with graph.as_default():