Omkar2118/datasets huggingface dataset:
Dayton
Full name: Dayton

The **Dayton** dataset is a dataset for ground-to-aerial (or aerial-to-ground) image translation, or cross-view image synthesis. It contains images of road views and aerial views of roads. There are 76,048 images in total and the train/test split is 55,000/21,048. The images in the original dataset have 354×354 resolution.

Source: [Multi-Channel Attention Selection GANs for Guided Image-to-Image Translation](https://arxiv.org/abs/2002.01048)
Image Source: [https://arxiv.org/abs/1912.06112](https://arxiv.org/abs/1912.06112)

Introducing paper: Localizing and Orienting Street Views Using Overhead Imagery
Set11
Full name: Set11

**Set11** is a dataset of 11 grayscale images. It is a dataset used for image reconstruction and image compression.

Source: [ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing](https://arxiv.org/abs/1706.07929)
Image Source: [https://arxiv.org/pdf/1706.07929.pdf](https://arxiv.org/pdf/1706.07929.pdf)

Introducing paper: ReconNet: Non-Iterative Reconstruction of Images From Compressively Sensed Measurements
GRID Dataset

The QMUL underGround Re-IDentification (**GRID**) dataset contains 250 pedestrian image pairs. Each pair contains two images of the same individual seen from different camera views. All images are captured from 8 disjoint camera views installed in a busy underground station. The figures beside show a snapshot of each of the camera views of the station and sample images in the dataset. The dataset is challenging due to variations of pose, colours, lighting changes; as well as poor image quality caused by low spatial resolution.

Source: [https://personal.ie.cuhk.edu.hk/~ccloy/downloads_qmul_underground_reid.html](https://personal.ie.cuhk.edu.hk/~ccloy/downloads_qmul_underground_reid.html)
ICVL Hand Posture
Full name: ICVL Hand Posture Dataset

The ICVL dataset is a hand pose estimation dataset that consists of 330K training frames and 2 testing sequences with each 800 frames. The dataset is collected from 10 different subjects with 16 hand joint annotations for each frame.

Source: [AWR: Adaptive Weighting Regression for 3D Hand Pose Estimation](https://arxiv.org/abs/2007.09590)
Image Source: [Tang et al.; Latent Regression Forest: Structured Estimation of 3D Hand Poses](https://alykhantejani.github.io/pdfs/LRF_PAMI_DRAFT.pdf)

Introducing paper: Latent Regression Forest: Structured Estimation of 3D Articulated Hand Posture