diff --git a/README.md b/README.md
index 6c409b7..86b590d 100644
--- a/README.md
+++ b/README.md
@@ -120,11 +120,11 @@ Below an example on a generic image from the web, created with:
 Download KITTI ground truth files and camera calibration matrices for training
 from [here](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d) and
 save them respectively into `data/kitti/gt` and `data/kitti/calib`. 
-To extract pifpaf joints, you also need to download training images, put it in any folder and soft link in `
+To extract pifpaf joints, you also need to download training images soft link the folder in `
 data/kitti/images`
 
 #### 2) nuScenes dataset
-Download nuScenes dataset from [nuScenes](https://www.nuscenes.org/download) (either Mini or Full), 
+Download nuScenes dataset from [nuScenes](https://www.nuscenes.org/download) (either Mini or TrainVal), 
 save it anywhere and soft link it in `data/nuscenes`
 
 
@@ -135,7 +135,7 @@ You can create them running the predict script and using `--networks pifpaf`.
 ### Inputs joints for training
 MonoLoco is trained using 2D human pose joints matched with the ground truth location provided by
 nuScenes or KITTI Dataset. To create the joints run: `python src/main.py prep` specifying:
-1. `--dir_ann` annotation directory containing pifpaf joints of kitti or nuScenes. 
+1. `--dir_ann` annotation directory containing Pifpaf joints of KITTI or nuScenes. 
 
 2. `--dataset` Which dataset to preprocess. For nuscenes, all three versions of the 
 dataset are supported: nuscenes_mini, nuscenes, nuscenes_teaser.
@@ -185,11 +185,13 @@ and save them into `data/kitti/monodepth`
 The best average value for comparison can be created running `python src/main.py eval --geometric`
 
 #### Evaluation
-First the model preprocess the joints starting from json annotations predicted from pifpaf, runs the model and save the results
+First the model preprocess the joints starting from json annotations predicted from pifpaf, 
+runs the model and save the results
 in txt file with format comparable to other baseline. 
 Then the model performs evaluation.
 
 The following graph is obtained running:
-`python3 src/main.py eval --dataset kitti --model data/models/base_model.pickle`
+`python3 src/main.py eval --dataset kitti --run_kitti --model data/models/monoloco-190513-1437.pkl 
+--dir_ann <folder containing pifpaf annotations of KITTI images>`
 ![kitti_evaluation](docs/results.png)
 
diff --git a/docs/002282.png.combined.png b/docs/002282.png.combined.png
deleted file mode 100644
index 33b893d..0000000
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diff --git a/docs/results.png b/docs/results.png
deleted file mode 100644
index 83c2a09..0000000
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diff --git a/src/eval/kitti_eval.py b/src/eval/kitti_eval.py
index 788997d..9436787 100644
--- a/src/eval/kitti_eval.py
+++ b/src/eval/kitti_eval.py
@@ -119,7 +119,7 @@ class KittiEval:
             print("\n Number of matched annotations: {:.1f} %".format(self.errors[key]['matched']))
             print("-"*100)
 
-    def print(self, show):
+    def printer(self, show):
         print_results(self.dic_stats, show)
 
     def _parse_txts(self, path, method):
diff --git a/src/eval/run_kitti.py b/src/eval/run_kitti.py
index e17fb59..d7b249d 100644
--- a/src/eval/run_kitti.py
+++ b/src/eval/run_kitti.py
@@ -17,6 +17,7 @@ class RunKitti:
         self.logger = logging.getLogger(__name__)
 
         # Set directories
+        assert dir_ann, "Annotations folder is required"
         self.dir_ann = dir_ann
         self.average_y = 0.48
         self.n_dropout = n_dropout
diff --git a/src/main.py b/src/main.py
index 9da7f57..e5c8640 100644
--- a/src/main.py
+++ b/src/main.py
@@ -145,7 +145,7 @@ def main():
         if args.dataset == 'kitti':
             kitti_eval = KittiEval()
             kitti_eval.run()
-            kitti_eval.print(show=args.show)
+            kitti_eval.printer(show=args.show)
 
         if 'nuscenes' in args.dataset:
             training = Trainer(joints=args.joints)
diff --git a/src/visuals/results.py b/src/visuals/results.py
index d29b9a5..0e618ef 100644
--- a/src/visuals/results.py
+++ b/src/visuals/results.py
@@ -31,7 +31,7 @@ def print_results(dic_stats, show=False, save=False):
     plt.xlabel("Distance [meters]")
     plt.ylabel("Average localization error [m]")
     plt.xlim(x_min, x_max)
-    labels = ['Mono3D', 'Geometric Baseline', 'MonoDepth', 'Our MonoLoco', '3DOP']
+    labels = ['Mono3D', 'Geometric Baseline', 'MonoDepth', 'Our MonoLoco', '3DOP (stereo)']
     mks = ['*', '^', 'p', 's', 'o']
     mksizes = [6, 6, 6, 6, 6]
     lws = [1.5, 1.5, 1.5, 2.2, 1.6]