Omkar2118/datasets huggingface dataset:
DeepHS Fruit v2

The data set covers recordings of ripening fruit with labels of destructive measurements (fruit flesh firmness, sugar content and overall ripeness).  The labels are provided within three categories (firmness, sweetness and overall ripeness).
Four measurement series were performed.  Besides 1018 labeled recordings, the data set contains 4671 recordings without ripeness label.  

The data set contains recordings of:
Avocados, Kiwis, Persimmons, Papayas, Mango

Three different hyperspectral cameras were used:
Specim FX 10,  INNO-SPEC Redeye 1.7, Corning microHSI 410 Vis-NIR Hyperspectral Sensor

Introducing paper: Measuring the Ripeness of Fruit with Hyperspectral Imaging and Deep Learning
REAP

**REAP** is a digital benchmark that allows the user to evaluate patch attacks on real images, and under real-world conditions. Built on top of the Mapillary Vistas dataset, the benchmark contains over 14,000 traffic signs. Each sign is augmented with a pair of geometric and lighting transformations, which can be used to apply a digitally generated patch realistically onto the sign.

Source: [REAP: A Large-Scale Realistic Adversarial Patch Benchmark](https://arxiv.org/pdf/2212.05680v1.pdf)

Image Source: [https://arxiv.org/pdf/2212.05680v1.pdf](https://arxiv.org/pdf/2212.05680v1.pdf)

Introducing paper: REAP: A Large-Scale Realistic Adversarial Patch Benchmark
BeautyFace

**BeautyFace** is a dataset containing 3,000 high-quality face images with a higher resolution of 512*512, covering more recent makeup styles and more diverse face poses, backgrounds, expressions, races, illumination. Each face has annotated parsing map.

Source: [BeautyREC: Robust, Efficient, and Component-Specific Makeup Transfer](https://arxiv.org/pdf/2212.05855v1.pdf)

Image Source: [https://arxiv.org/pdf/2212.05855v1.pdf](https://arxiv.org/pdf/2212.05855v1.pdf)

Introducing paper: BeautyREC: Robust, Efficient, and Content-preserving Makeup Transfer
Accidental Turntables

**Accidental Turntables** contains a challenging set of 41,212 images of cars in cluttered backgrounds, motion blur and illumination changes that serves as a benchmark for 3D pose estimation.

Source: [Accidental Turntables: Learning 3D Pose by Watching Objects Turn](https://arxiv.org/pdf/2212.06300v1.pdf)

Image Source: [https://people.cs.umass.edu/~zezhoucheng/acci-turn/](https://people.cs.umass.edu/~zezhoucheng/acci-turn/)

Introducing paper: Accidental Turntables: Learning 3D Pose by Watching Objects Turn