Omkar2118/datasets huggingface dataset:
PubTabNet
Full name: PubTabNet

PubTabNet is a large dataset for image-based table recognition, containing 568k+ images of tabular data annotated with the corresponding HTML representation of the tables. The table images are extracted from the scientific publications included in the PubMed Central Open Access Subset (commercial use collection). Table regions are identified by matching the PDF format and the XML format of the articles in the PubMed Central Open Access Subset. More details are available in our paper "Image-based table recognition: data, model, and evaluation".

Introducing paper: Image-based table recognition: data, model, and evaluation
Oximeter Image Dataset | Medical Device Reading
Full name: datacluster.ai

This dataset is an extremely challenging set of over 2000+ original Oximeter images captured and crowdsourced from over 300+ urban and rural areas, where each image is **manually reviewed and verified** by computer vision professionals at Datacluster Labs.

### **Dataset Features**

- Dataset size   : 2000+
- Captured by   : Over 300+ crowdsource contributors
- Resolution      : 100% images HD and above (1920x1080 and above)
- Location         : Captured with 50+ cities accross India
- Diversity         : Various lighting conditions like day, night, varied distances, view points etc.
- Device used   : Captured using mobile phones in 2020-2021
- Usage             : Oximeter Reading detection, Medical Device, Healthcare system detection, etc.

### Available Annotation formats

COCO, YOLO, PASCAL-VOC, Tf-Record

**To download full datasets or to submit a request for your dataset needs, please ping us at [sales@datacluster.ai](sales@datacluster.ai) Visit [www.datacluster.ai](www.datacluster.ai) to know more.**  

**Note**:  
All the images are manually captured and verified by a large contributor base on DataCluster platform
ICM
Full name: Image-Caption Matching Dataset

ICM is curated for the image-text matching task. Each image has a corresponding caption text, which describes the image in detail. We first use CTR to select the most relevant pairs. Then, human annotators manually perform a 2nd round manual correction, obtaining 400,000 image-text pairs, including 200,000 positive cases and 200,000 negative cases. We keep the ratio of positive and negative pairs consistent in each of the train/val/test sets.

Introducing paper: CCMB: A Large-scale Chinese Cross-modal Benchmark
IQM
Full name: Image-Query Matching Dataset

IQM is curated for the image-text matching task. Each image has a corresponding search query. We first use CTR to select the most relevant pairs. In this dataset, we randomly select image-query pairs in the candidate set after performing the cleaning process, obtaining 400,000 image-text pairs, including 200,000 positive cases and 200,000 negative cases. We keep the ratio of positive and negative pairs consistent in each of the train/val/test sets.

Introducing paper: CCMB: A Large-scale Chinese Cross-modal Benchmark