Omkar2118/datasets huggingface dataset:
VOTChallenge
Full name: Visual Object Tracking

The Visual Object Tracking (VOT) dataset is a collection of video sequences used for evaluating and benchmarking visual object tracking algorithms. It provides a standardized platform for researchers and practitioners to assess the performance of different tracking methods.
OOD-CV
Full name: Out Of Distribution Generalization in Computer Vision

Enhancing the robustness of vision algorithms in real-world scenarios is challenging. One reason is that existing robustness benchmarks are limited, as they either rely on synthetic data or ignore the effects of individual nuisance factors. We introduce OOD-CV, a benchmark dataset that includes out-of-distribution examples of 10 object categories in terms of pose, shape, texture, context and the weather conditions, and enables benchmarking models for image classification, object detection, and 3D pose estimation. In addition to this novel dataset, we contribute extensive experiments using popular baseline methods, which reveal that: 1. Some nuisance factors have a much stronger negative effect on the performance compared to others, also depending on the vision task. 2. Current approaches to enhance robustness have only marginal effects, and can even reduce robustness. 3. We do not observe significant differences between convolutional and transformer architectures. We believe our dataset provides a rich test bed to study robustness and will help push forward research in this area.

Introducing paper: OOD-CV: A Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images
CALLHOME American English Speech

The CALLHOME English Corpus is a collection of unscripted telephone conversations between native speakers of English. Here are the key details:

Participants: 120 individuals.
Type of Study: Naturalistic.
Location: USA.
Media Type: Audio.
DOI: doi:10.21415/T5KP54.
Contents: The corpus contains 120 telephone conversations, each lasting up to 30 minutes.
Origins: All calls originated in North America, with 90 calls placed to various locations overseas and 30 calls within North America.
Transcripts: The transcripts cover contiguous 5 or 10-minute segments from recorded conversations.
Speaker Awareness: All speakers were aware that they were being recorded.
Topics: Participants had no guidelines on what to talk about; most called family members or close friends overseas.
Purpose: Collected primarily to support the project on Large Vocabulary Conversational Speech Recognition (LVCSR), sponsored by the U.S. Department of Defense.
ABODA
Full name: Abandoned Object Dataset

**ABandoned Objects DAtaset (ABODA)** is a new public dataset for abandoned object detection. ABODA comprises 11 sequences labeled with various real-application scenarios that are challenging for abandoned-object detection. The situations include crowded scenes, marked changes in lighting condition, night-time detection, as well as indoor and outdoor environments.

Source: [https://github.com/kevinlin311tw/ABODA](https://github.com/kevinlin311tw/ABODA)
Dark Corner Artifact Masks for ISIC Images

A set of over 4000 dark corner artifact / vignette masks that can be applied to ISIC skin lesion images to test for the effect of such artifacts in classification tasks.