Omkar2118/datasets huggingface dataset:
1DSfM
Full name: 1DSfM Landmarks

The 1DSfM Landmarks is a collection of community-based image reconstruction by Kyle Wilson and is comprised of 14 datasets with comparison to bundler ground truth. Notredame is provided separately. Datasets (tar.gz, 642 MB) Alamo images (tar, 2.0 GB) Ellis Island images (tar, 1.6 GB) Madrid Metropolis images (tar, 0.7 GB) Montreal Notre Dame images (tar, 1.6 GB) NYC_Library images (tar, 1.6 GB) Piazza del Popolo images (tar, 1.5 GB) Piccadilly images (tar, 3.7 GB) Roman Forum images (tar, 1.5 GB) Tower of London images (tar, 1.1 GB) Trafalgar images (tar, 8.5 GB) Union Square images (tar, 3.6 GB) Vienna Cathedral images (tar, 3.3 GB) Yorkminster images (tar, 2.2 GB) Gendarmenmarkt images (tar, 1.0 GB) References Robust Global Translations with 1DSfM Kyle Wilson and Noah Snavely, ECCV 2014
COCO-MLT

The COCO-MLT is created from MS COCO-2017, containing 1,909 images from 80 classes. The maximum of training number per class is 1,128 and the minimum is 6. We use the test set of COCO2017 with 5,000 for evaluation. The ratio of head, medium, and tail classes is 22:33:25 in COCO-MLT.

Introducing paper: Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets
VOC-MLT

We construct the long-tailed version of VOC  from its 2012 train-val set. It contains 1,142 images from 20 classes, with a maximum of 775 images per class and a minimum of 4 images per class.  The ratio of head, medium, and tail classes after splitting is 6:6:8. We evaluate the performance on VOC2007 test set with 4952 images.

Introducing paper: Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets
CHAD
Full name: Charlotte Anomaly Dataset

# CHAD: Charlotte Anomaly Dataset

CHAD is high-resolution, multi-camera dataset for surveillance video anomaly detection. It includes bounding box, Re-ID, and pose annotations, as well as frame-level anomaly labels, dividing all frames into two groups of anomalous or normal. You can find the paper with all the details in the following link: [**CHAD: Charlotte Anomaly Dataset**](https://arxiv.org/abs/2212.09258 "CHAD Paper"). Please refer to the page of the dataset for more information.

Introducing paper: CHAD: Charlotte Anomaly Dataset