It's been a while since the dataset has been published. There has been a request for this dataset yesterday and I realised the dataset is gone!
The pointers to the original dataset and our ICCV'15 splits are as follows:
- https://people.eecs.berkeley.edu/~nzhang/piper.html
- https://www.mpi-inf.mpg.de/departments/computer-vision-and-machine-learning/research/people-detection-pose-estimation-and-tracking/person-recognition-in-personal-photo-collections
They are not working any longer. Please use this Github repository as a guide.
We only have access to metadata, not the original images. Fortunately, PIPA dataset is built exclusively on Flickr and Flickr images are accessible with the Photo IDs in our metadata. See all_data.txt. It shows the full list of annotated person instances in the PIPA dataset. The second column corresponds to the Photo ID for each instance.
You can crawl the images from Flickr using the Photo IDs. See https://www.flickr.com/services/api/ for details.
On a browser, one may search the images with Photo ID by: https://www.flickr.com/photo.gne?id=<Photo_ID>.
Example: https://www.flickr.com/photo.gne?id=12990166725
Update: I have added an example crawler. There seem to be some missing photos on Flickr. It is also unclear which resolution the original PIPA dataset has been made up of (see https://www.flickr.com/services/api/flickr.photos.getSizes.html for different sizes available for each photo).
The whole dataset is split into train, val, test, and leftover, with disjoint person identities. They correspond to column 8 in all_data.txt (values 0, 1, 2, 3, respectively).
The val and test sets are further divided into two splits (e.g. val0 and val1). val0 is meant as a person-specific training set ("registering" identities) and val1 as the evaluation set for each identity. Same goes for test0 and test1.
I know this is complicated.. It's probably better to describe a bit on how people have been using this dataset. People have typically pre-trained their CNNs on the train set for generic identities and then trained person-specific multi-class SVM on top of the CNN features using val0 or test0 sets. The CNN + SVM are then evaluated on val1 or test1.
Each person instance is annotated with a head bounding box and an integer ID (same ID for same person).
Box: columns 3-6 in all_data.txt in the order (x,y,w,h).
ID: column 7 in all_data.txt
We have introduced four splits (val0 vs val1 or test0 vs test1) corresponding to different difficulty levels (different time gaps between training & test images per person).
Original: Train and test a person's appearance on the same days (proposed in the original PIPA).
Album: Train a person's appearance in one album, test on the other album.
Time: Train a person's appearance in the earliest time range, test on the latest time range (using photo-taken-date metadata).
Day: Train and test a person's appearance on different days (manually separated).
You can find the splits in files of the form split_{train,val}_{original,album,time,day}.txt. Each file is organized as
<photoset_id> <photo_id> <xmin> <ymin> <width> <height> <identity_id> <subset_id> <SPLIT(0/1)>
Following the PIPA protocol, your identity classifier must be trained on split 0 instances and be tested on split 1 instances. Then, do the same with the split 0 and 1 swapped. For evaluation, take the average of the these performances.
@inproceedings{zhang2015beyond,
title={Beyond frontal faces: Improving person recognition using multiple cues},
author={Zhang, Ning and Paluri, Manohar and Taigman, Yaniv and Fergus, Rob and Bourdev, Lubomir},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={4804--4813},
year={2015}
}
@inproceedings{joon2015person,
title={Person recognition in personal photo collections},
author={Joon Oh, Seong and Benenson, Rodrigo and Fritz, Mario and Schiele, Bernt},
booktitle={Proceedings of the IEEE international conference on computer vision},
pages={3862--3870},
year={2015}
}