Omkar2118/datasets huggingface dataset:
Completion3D

The Completion3D benchmark is a dataset for evaluating state-of-the-art 3D Object Point Cloud Completion methods. Ggiven a partial 3D object point cloud the goal is to infer a complete 3D point cloud for the object.

Source: [TopNet: Structural Point Cloud Decoder](/paper/topnet-structural-point-cloud-decoder)

Introducing paper: TopNet: Structural Point Cloud Decoder
QMNIST

The exact pre-processing steps used to construct the MNIST dataset have long been lost. This leaves us with no reliable way to associate its characters with the ID of the writer and little hope to recover the full MNIST testing set that had 60K images but was never released. The official MNIST testing set only contains 10K randomly sampled images and is often considered too small to provide meaningful confidence intervals.
The **QMNIST** dataset was generated from the original data found in the NIST Special Database 19 with the goal to match the MNIST preprocessing as closely as possible.
QMNIST is licensed under the BSD-style license.

Source: [https://github.com/facebookresearch/qmnist](https://github.com/facebookresearch/qmnist)
Image Source: [https://github.com/facebookresearch/qmnist](https://github.com/facebookresearch/qmnist)

Introducing paper: Cold Case: The Lost MNIST Digits
ePillID

**ePillID** is a benchmark for developing and evaluating computer vision models for pill identification. The ePillID benchmark is designed as a low-shot fine-grained benchmark, reflecting real-world challenges for developing image-based pill identification systems.
The characteristics of the ePillID benchmark include:
* Reference and consumer images: The reference images are taken with controlled lighting and backgrounds, and with professional equipment. The consumer images are taken with real-world settings including different lighting, backgrounds, and equipment. For most of the pills, one image per side (two images per pill type) is available from the NIH Pillbox dataset.
* Low-shot and fine-grained setting: 13k images representing 9804 appearance classes (two sides for 4902 pill types). For most of the appearance classes, there exists only one reference image, making it a challenging low-shot recognition setting.

Source: [https://github.com/usuyama/ePillID-benchmark](https://github.com/usuyama/ePillID-benchmark)
Image Source: [https://github.com/usuyama/ePillID-benchmark](https://github.com/usuyama/ePillID-benchmark)

Introducing paper: ePillID Dataset: A Low-Shot Fine-Grained Benchmark for Pill Identification