Update README.md

README.md

@@ -63,6 +63,8 @@ dataset_summary: >
 
 This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 846 samples.
 
+**Note:** The images here are from the test set of the [original dataset](http://domedb.perception.cs.cmu.edu/panopticDB/hands/hand_labels.zip) and parsed into FiftyOne format.
+
 ## Installation
 
 If you haven't already, install FiftyOne:
@@ -88,12 +90,19 @@ session = fo.launch_app(dataset)
 
 ## Dataset Details
 
-As part of their research, the authors created a dataset by manually annotating two publicly available image sets: the MPII Human Pose dataset and images from the New Zealand Sign Language (NZSL) Exercises. To date, they collected annotations for 1300 hands on the MPII set and 1500 on NZSL. This combined dataset was split into a training set (2000 hands) and a testing set (800 hands)
+As part of their research, the authors created a dataset by manually annotating two publicly available image sets: the MPII Human Pose dataset and images from the New Zealand Sign Language (NZSL) Exercises. 
+
+To date, they collected annotations for 1300 hands on the MPII set and 1500 on NZSL. 
 
+This combined dataset was split into a training set (2000 hands) and a testing set (800 hands)
 
 ### Dataset Description
 
-The dataset created in this research is a collection of manually annotated RGB images of hands sourced from the MPII Human Pose dataset and the New Zealand Sign Language (NZSL) Exercises. It contains 2D locations for 21 keypoints on 2800 hands, split into a training set of 2000 hands and a testing set of 800 hands. This dataset was used to train and evaluate their hand keypoint detection methods for single images.
+The dataset created in this research is a collection of manually annotated RGB images of hands sourced from the MPII Human Pose dataset and the New Zealand Sign Language (NZSL) Exercises. 
+
+It contains 2D locations for 21 keypoints on 2800 hands, split into a training set of 2000 hands and a testing set of 800 hands. 
+
+This dataset was used to train and evaluate their hand keypoint detection methods for single images.
 
 - **Paper:** https://arxiv.org/abs/1704.07809
 - **Demo:** http://domedb.perception.cs.cmu.edu/handdb.html
@@ -108,7 +117,15 @@ The dataset created in this research is a collection of manually annotated RGB i
 
 ### Direct Use
 
-This manually annotated dataset was directly used to train and evaluate their hand keypoint detection methods. The dataset serves as a benchmark to assess the accuracy of their single image 2D hand keypoint detector. It enabled them to train an initial detector and evaluate the improvements gained through their proposed multiview bootstrapping technique. The dataset contains images extracted from YouTube videos depicting everyday human activities (MPII) and images showing a variety of hand poses from people using New Zealand Sign Language (NZSL). These diverse sets of images allowed the researchers to evaluate the generalization capabilities of their detector.
+This manually annotated dataset was directly used to train and evaluate their hand keypoint detection methods. 
+
+The dataset serves as a benchmark to assess the accuracy of their single image 2D hand keypoint detector. 
+
+It enabled them to train an initial detector and evaluate the improvements gained through their proposed multiview bootstrapping technique. 
+
+The dataset contains images extracted from YouTube videos depicting everyday human activities (MPII) and images showing a variety of hand poses from people using New Zealand Sign Language (NZSL). 
+
+These diverse sets of images allowed the researchers to evaluate the generalization capabilities of their detector.
 
 ## Dataset Structure
 
@@ -149,9 +166,17 @@ The dataset is composed of manually annotated RGB images of hands sourced from t
 
 #### Annotation process
 
-This manually annotated dataset was directly used to train and evaluate their hand keypoint detection methods. The dataset serves as a benchmark to assess the accuracy of their single image 2D hand keypoint detector. It enabled them to train an initial detector and evaluate the improvements gained through their proposed multiview bootstrapping technique. The dataset contains images extracted from YouTube videos depicting everyday human activities (MPII) and images showing a variety of hand poses from people using New Zealand Sign Language (NZSL). These diverse sets of images allowed the researchers to evaluate the generalization capabilities of their detector
-The process of manually annotating hand keypoints in single images was challenging due to frequent occlusions caused by hand articulation, viewpoint, and grasped objects (as illustrated in Fig. 2). In many cases, annotators had to estimate the locations of occluded keypoints, potentially reducing the accuracy of these annotations
+This manually annotated dataset was directly used to train and evaluate their hand keypoint detection methods. 
+
+The dataset serves as a benchmark to assess the accuracy of their single image 2D hand keypoint detector. It enabled them to train an initial detector and evaluate the improvements gained through their proposed multiview bootstrapping technique. 
+
+The dataset contains images extracted from YouTube videos depicting everyday human activities (MPII) and images showing a variety of hand poses from people using New Zealand Sign Language (NZSL). 
+
+These diverse sets of images allowed the researchers to evaluate the generalization capabilities of their detector.
+
+ The process of manually annotating hand keypoints in single images was challenging due to frequent occlusions caused by hand articulation, viewpoint, and grasped objects. 
 
+ In many cases, annotators had to estimate the locations of occluded keypoints, potentially reducing the accuracy of these annotations
 ## Citation 
 
 ```bibtex