Omkar2118/datasets huggingface dataset:
LAION-Aesthetics V2 6.5+

* A subset of the LAION 5B samples with English captions, obtained using LAION-Aesthetics_Predictor V2
* 625K image-text pairs with predicted aesthetics scores of 6.5 or higher
* available at https://huggingface.co/datasets/ChristophSchuhmann/improved_aesthetics_6.5plus
DFEW
Full name: Dynamic Facial Expression in the Wild

Recently, facial expression recognition (FER) in the wild has gained
a lot of researchers’ attention because it is a valuable topic to enable the FER techniques to move from the laboratory to the real
applications. In this paper, we focus on this challenging but interesting topic and make contributions from three aspects. First, we
present a new large-scale ’in-the-wild’ dynamic facial expression
database, DFEW (Dynamic Facial Expression in the Wild), consisting of over 16,000 video clips from thousands of movies. These
video clips contain various challenging interferences in practical
scenarios such as extreme illumination, occlusions, and capricious
pose changes. Second, we propose a novel method called ExpressionClustered Spatiotemporal Feature Learning (EC-STFL) framework
to deal with dynamic FER in the wild. Third, we conduct extensive
benchmark experiments on DFEW using a lot of spatiotemporal
deep feature learning methods as well as our proposed EC-STFL.
Experimental results show that DFEW is a well-designed and challenging database, and the proposed EC-STFL can promisingly improve the performance of existing spatiotemporal deep neural networks in coping with the problem of dynamic FER in the wild. Our
DFEW database is publicly available and can be freely downloaded
from https://dfew-dataset.github.io/.

Introducing paper: DFEW: A Large-Scale Database for Recognizing Dynamic Facial Expressions in the Wild
FERV39k

Current benchmarks for facial expression recognition (FER) mainly focus on static images, while there are limited datasets for FER in videos. It is still ambiguous to evaluate whether performances of existing methods remain satisfactory in real-world application-oriented scenes. For example, the “Happy” expression with high intensity in Talk-Show is more discriminating than the same expression with low intensity in Official-Event. To fill this gap, we build a large-scale multi-scene dataset, coined as FERV39k. We analyze the important ingredients of constructing such a novel dataset in three aspects: (1) multi-scene hierarchy and expression class, (2) generation of candidate video clips, (3) trusted manual labelling process. Based on these guidelines, we select 4 scenarios subdivided into 22 scenes, annotate 86k samples automatically obtained from 4k videos based on the welldesigned workflow, and finally build 38,935 video clips labeled with 7 classic expressions. Experiment benchmarks on four kinds of baseline frameworks were also provided and further analysis on their performance across different scenes and some challenges for future research were given. Besides, we systematically investigate key components of DFER by ablation studies. The baseline framework and our project are available on url.

Introducing paper: FERV39k: A Large-Scale Multi-Scene Dataset for Facial Expression Recognition in Videos