Update README.md

README.md

@@ -10,7 +10,7 @@ This repository hosts the code and data for our paper: N. Gkalelis, A. Goulas, D
 
 ## Video preprocessing
 
-Before training our method on any video dataset, the videos must be preprocessed and converted to an appropriate format for efficient data loading (In our work, we sample 9 frames per video; on each frame, a variant of the Faster R-CNN object detector is used [3,4] for object detection, and an ResNet-152 network is used for extracting a representation of each entire frame as well as each object region). Following video preprocessing, the dataset root directory must contain the following two subdirectories:
+Before training our method on any video dataset, the videos must be preprocessed and converted to an appropriate format for efficient data loading (in our work, we sample 9 frames per video; on each frame, a variant of the Faster R-CNN object detector is used [3,4] for object detection, and an ResNet-152 network is used for extracting a representation of each entire frame as well as each object region). Following video preprocessing, the dataset root directory must contain the following two subdirectories:
 * ```R152_global/```: Numpy arrays of size 9x2048 containing the global frame feature vectors for each video (the 9 frames, times the 2048-element vector for each frame).
 * ```R152/```: Numpy arrays of size 9x50x2048 containing the appearance feature vectors of the detected frame objects for each video (the 9 frames, times the 50 most-promiment objects identified by the obejct detector, times a 2048-element vector for each object bounding box).