diff --git a/readme.md b/readme.md
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--- a/readme.md
+++ b/readme.md
@@ -8,7 +8,7 @@ The training script can be run by calling `train.py` with the name of the experi
 ```
 python train.py name-of-experiment --gpu 0
 ```
-By default data will be read from `data/kitti/objects` and model checkpoints will be saved to `experiments`. The model is trained using the KITTI 3D object detection benchmark which can be downloaded from [here](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d). See `train.py` for a full list of training options.
+By default data will be read from `data/kitti/object` and model checkpoints will be saved to `experiments`. The model is trained using the KITTI 3D object detection benchmark which can be downloaded from [here](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d). See `train.py` for a full list of training options.
 
 ## Inference
 To decode the network predictions and visualise the resulting bounding boxes, run the `infer.py` script with the path to the model checkpoint you wish to visualise: