Small changes

README.md

@@ -23,7 +23,7 @@ Workshop AVVision : Autonomous Vehicle Vision
 - [Acknowledgements](#acknowledgements)
 
 # Overview
-This repository contains materials from the paper
+This repository contains the materials presented in the paper
 [DriPE: A Dataset for Human Pose Estimation in Real-World Driving Settings](https://openaccess.thecvf.com/content/ICCV2021W/AVVision/papers/Guesdon_DriPE_A_Dataset_for_Human_Pose_Estimation_in_Real-World_Driving_ICCVW_2021_paper.pdf).
 
 We provide the link to download the DriPE [dataset](#dataset),
@@ -32,13 +32,13 @@ SBl, MSPN and RSN.
 Furthermore, we provide the code to evaluate HPE networks with [mAPK metric](#evaluation), our keypoint-centered metric.
 
 # Dataset
-DriPE dataset can be found [here](http://dionysos.univ-lyon2.fr/~ccrispim/DriPE/DriPE.zip). We provide the 10k images, 
+DriPE dataset can be download [here](http://dionysos.univ-lyon2.fr/~ccrispim/DriPE/DriPE.zip). We provide 10k images, 
 along with keypoint annotations, split as:
 * 6.4k for training
 * 1.3k for validation
 * 1.3k for testing
 
-Annotations follow the COCO annotation style, with 17 keypoints. 
+The annotation files follow the COCO annotation style, with 17 keypoints. 
 More information can be found [here](https://cocodataset.org/#format-data).
 
 ##### **DriPE image samples**
@@ -81,7 +81,7 @@ Paths can be absolute, relative to the script or relative to the respective json
     -h, --help\tdisplay this help message and exit
 ```
 
-We provide in this repo one annotation file and one prediction. To evaluate these predictions, run:
+We provide in this repo one annotation and one prediction file. To evaluate these predictions, run:
 ```
 python eval_mapk.py keypoints_out_SBL_autob_test-repo.json autob_coco_test.json
 ```
@@ -108,8 +108,7 @@ If you use this dataset or code in your research, please cite the paper:
 ```
 
 # Acknowledgments
-This work was supported by the Pack Ambition
-Recherche 2019 funding of the French AURA Region in
+This work was supported by the Pack Ambition Recherche 2019 funding of the French AURA Region in
 the context of the AutoBehave project.
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