Merge pull request #69 from six2dez/patch-2

Update requirements.txt

README.md

@@ -7,6 +7,7 @@ Give those screenshots of yours a quick eyeballing.
 
 Eyeballer is meant for large-scope network penetration tests where you need to find "interesting" targets from a huge set of web-based hosts. Go ahead and use your favorite screenshotting tool like normal (EyeWitness or GoWitness) and then run them through Eyeballer to tell you what's likely to contain vulnerabilities, and what isn't.
 
+Give it a try live at: https://eyeballer.bishopfox.com
 
 ### Example Labels
 
@@ -59,15 +60,20 @@ For the latest pretrained weights, check out the releases here on GitHub.
 
 **Training Data** You can find our training data here:
 
-https://www.dropbox.com/s/rpylhiv2g0kokts/eyeballer-3.0.zip?dl=1
+https://www.kaggle.com/altf42600/pentest-screensots
 
 There's two things you need from the training data:
 
 1. `images/` folder, containing all the screenshots (resized down to 224x224)
 2. `labels.csv` that has all the labels
-3. `bishop-fox-pretrained-v3.h5` A pretrained weights file you can use right out of the box without training.
 
-Copy all three into the root of the Eyeballer code tree.
+Copy both into the root of the Eyeballer code tree.
+
+Additionally, you can find a pretrained weights file you can use right out of the box without training.  
+
+1. `bishop-fox-pretrained-vN.h5`
+
+It's here on GitHub, look at the `releases` section for the latest one.
 
 ## Predicting Labels
 

requirements.txt

@@ -4,7 +4,8 @@ matplotlib
 numpy
 pandas
 pillow
-sklearn
+scikit-learn
 tensorflow
 jinja2
 progressbar2
+scipy

utils/verify.py

@@ -15,11 +15,14 @@ with open("labels.csv", newline="") as csvfile:
     for row in csvreader:
         label_list.append(row["filename"])
 
+    print(len(file_list), "files")
+    print(len(label_list), "labels")
+
     # Loop through the file
     print("Rows in labels.csv, but no image exists:")
     for label in label_list:
         if label not in file_list:
-            print(row["filename"])
+            print(label)
 
     print("Images that exist, but don't have labels:")
     i = 0