LaFin: Large-scale Flickr interestingness

 dataset (hereafter “the Dataset”) is a collection of Flickr image IDs corresponding to about 123k Flickr images, equally balanced between interesting and non-interesting images, and their corresponding metadata. In addition to the images, their binary labels, and associated metadata, some precomputed features are provided: CNNs, semantic features that derived from image captioning and Word2Vec representations of Flickr tags. 

It is intended to be used for analyzing socially-driven image interestingness and building a machine learning model for prediction. A detailed description of the benchmark can be found on our Data Description page. The license conditions are mentioned on the Download page.

All documents and papers that report on research that uses the LAFIN Image Interestingness Dataset must acknowledge the use of the dataset by including an appropriate citation to the following:

E.Berson, N. Q. K. Duong and C.-H. Demarty. Collecting, Analyzing and Predicting Socially-Driven Image Interestingness. In Proceedings of the EUSIPCO Conference, Spain, 2019.

Title = { Collecting, Analyzing and Predicting Socially-Driven Image Interestingness},

Author = {Elois Berson and Ngoc Q.K. Duong and Claire-H\'{e}l\`{e}ne Demarty}, Booktitle = {Proc. of the EUSIPCO conference, Spain}, Year = {2019},

It would be highly appreciated if this use was shared with InterDigital, at this address: 

The delivered package contains the dataset used in [1]. It was intended for the tasks of socially-driven image interestingness understanding and prediction. It is composed of:

A list of Flickr image IDs corresponding to 123k Flickr images with associated metadata

Extracted image and text features that were used in [1].

The complete list of Flickr image IDs and their corresponding label in file 

The set of image and text features used to train the model presented in [1] is provided together with the data:

Image-captioning based features (IC) extracted for each image;

Word2Vec features extracted from the Flickr tags associated with each image.

These features are extracted from the VGG16 network [2] pre-trained on the ImageNet dataset. For each image, a CNN feature is extracted from the last fully-connected layer before the softmax and has a dimension of 4096.

These features are computed by an image captioning (IC) system as described in [3], which has an encoder comprising a CNN and a long short-term memory recurrent network (LSTM) for learning a joint image-text embedding. The projected CNN feature for each image is extracted and has a dimension of 1024. 

A 300-dimensional word embedding vector [3] for each tag associated with one image is provided. Those Word2Vec features are then averaged to obtain a single feature vector per image.

 E. Berson, N. Q. K. Duong and C.-H. Demarty. "Collecting, Analyzing and Predicting Socially-Driven Image Interestingness." In Proceedings of the EUSIPCO Conference, Spain, 2019.

 R. Kiros, R. Salakhutdinov, and R. Zemel, “Unifying visual-semantic embeddings with multimodal neural language models,” CoRR, vol.abs/1411.2539, 2014. [Online]. Available: http://arxiv.org/abs/1411.2539

 T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” in Proceedings of Workshop at ICLR, 2013.

In order to get the data (Flickr image IDs, metadata, extracted features, and the interestingness labels), you are asked to supply your name and email address. You will receive instructions on how to download the dataset via this email address. We may store the data you supplied in order to contact you later about benchmark related matters. The data will not be used in any other way.

To download the LAFIN dataset please send an email to 

. By doing this you irrevocably agree to any and all provision of the license agreement on this page.

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LaFin Dataset

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