Omkar2118/datasets huggingface dataset:
OmniObject3D

**OmniObject3D** is a large vocabulary 3D object dataset with massive high-quality real-scanned 3D objects. OmniObject3D has several appealing properties: 

1) Large Vocabulary: It comprises 6,000 scanned objects in 190 daily categories, sharing common classes with popular 2D datasets (e.g., ImageNet and LVIS), benefiting the pursuit of generalizable 3D representations. 

2) Rich Annotations: Each 3D object is captured with both 2D and 3D sensors, providing textured meshes, point clouds, multiview rendered images, and multiple real-captured videos. 

3) Realistic Scans: The professional scanners support highquality object scans with precise shapes and realistic appearances.

Introducing paper: OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generation
SPEC5G

**SPEC5G** is a dataset for the analysis of natural language specification of 5G Cellular network protocol specification. SPEC5G contains 3,547,587 sentences with 134M words, from 13094 cellular network specifications and 13 online websites. It is designed for security-related text classification and summarisation.

Introducing paper: SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis
CMMD
Full name: The Chinese Mammography Database

Breast carcinoma is the second largest cancer in the world among women. Early detection of breast cancer has been shown to increase the survival rate, thereby significantly increasing patients' lifespans. Mammography, a noninvasive imaging tool with low cost, is widely used to diagnose breast disease at an early stage due to its high sensitivity. The recent popularization of artificial intelligence in computer-aided diagnosis creates opportunities for advances in areas such as (1) Computer-aided detection for locating suspect lesions such as mass and microcalcification, leaving the classification to the radiologist; and (2) Computer-aided diagnosis for characterizing the suspicious region of lesion and/or estimate its probability of onset; and (3) Findings of predictive image-based biomarkers by applying the computational methods to mine the potential relationships between image representation and molecular subtype, including luminal A, luminal B, HER2 positive, and Triple-negative.

However, existing publicly available mammography databases are limited by small sample size, lack of diversity in patient populations, missing biopsy confirmations and unknown molecular sub-types.  To help fill the gap, we built a database conducted on 1,775 patients from China with benign or malignant breast disease who underwent mammography examination between July 2012 and January 2016. The database consists of 3,728 mammographies from these 1,775 patients, with biopsy confirmed type of benign or malignant tumors. For 749 of these patients (1,498 mammographies) we also include patients' molecular subtypes. Image data were acquired on a GE Senographe DS mammography system.  

Publication Citation

Cai, H., Huang, Q., Rong, W., Song, Y., Li, J., Wang, J., Chen, J., & Li, L. (2019). Breast Microcalcification Diagnosis Using Deep Convolutional Neural Network from Digital Mammograms. Computational and Mathematical Methods in Medicine, 2019, 1–10. https://doi.org/10.1155/2019/2717454

Wang, J., Yang, X., Cai, H., Tan, W., Jin, C., & Li, L. (2016). Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning. Scientific Reports, 6(1). https://doi.org/10.1038/srep27327

Data Citation
Cui, Chunyan; Li Li; Cai, Hongmin; Fan, Zhihao; Zhang, Ling; Dan, Tingting; Li, Jiao; Wang, Jinghua. (2021) The Chinese Mammography Database (CMMD): An online mammography database with biopsy confirmed types for machine diagnosis of breast. The Cancer Imaging Archive. DOI: https://doi.org/10.7937/tcia.eqde-4b16

TCIA Citation
Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging, 26(6), 1045–1057. https://doi.org/10.1007/s10278-013-9622-7