Prior livestock research provides evidence for the importance of accurate detection of pig positions and postures for better understanding animal welfare. Position and posture detection can be accomplished by machine vision systems. However, current machine vision systems require rigid setups of fixed vertical lighting, vertical top-view camera perspectives or complex camera systems, which hinder their adoption in practice. Moreover, existing detection systems focus on specific pen contexts and may be difficult to apply in other livestock facilities. Our main contribution is twofold: First, we design a deep learning system for position and posture detection that only requires standard 2D camera imaging with no adaptations to the application setting. This deep learning system applies the state-of-the-art Faster R-CNN object detection pipeline and the state-of-the-art Neural Architecture Search (NAS) base network for feature extraction. Second, we provide a labelled open access dataset with 7277 human-made annotations from 21 standard 2D cameras, covering 31 different one-hour long video recordings and 18 different pens to train and test the approach under realistic conditions. On unseen pens under similar experimental conditions with sufficient similar training images of pig fattening, the deep learning system detects pig position with an Average Precision (AP) of 87.4%, and pig position and posture with a mean Average Precision (mAP) of 80.2%. Given different and more difficult experimental conditions of pig rearing with no or little similar images in the training set, an AP of over 67.7% was achieved for position detection. However, detecting the position and posture achieved a mAP between 44.8% and 58.8% only. Furthermore, we demonstrate exemplary applications that can aid pen design by visualizing where pigs are lying and how their lying behavior changes through the day. Finally, we contribute open data that can be used for further studies, replication, and pig position detection applications.