Omkar2118/datasets huggingface dataset:
OVT-B
Full name: Open-Vocabulary multi-object Tracking Benchmark

OVT-B, a large-scale Open-Vocabulary multi-object Tracking Benchmark containing 1,973 videos and 637,608 annotated objects from 1,048 categories, surpassing the diversity of all current MOT datasets. OVT-B also includes some attributes especially for the MOT task, e.g., the scenarios of out-of-view, fast motion, mutual occlusion, and objects with various sizes, shapes, etc.

Introducing paper: OVT-B: A New Large-Scale Benchmark for Open-Vocabulary Multi-Object Tracking
Car_Price_Prediction
Full name: Second_Hand-Car_Price_Prediction

In this dataset we added [Company Name, 
                                                      Car Model, 
                                                      Car Type, 
                                                      Fuel Type, 
                                                      Transmission, 
                                                      Engine (cc), 
                                                      Mileage, 
                                                      Kms_driven, 
                                                      Buyers, 
                                                      Horsepower (kw), 
                                                      Year Price (Lakhs)]
CCI 3.0-HQ

To address the scarcity of high-quality safety datasets in the Chinese, we open-sourced the CCI (Chinese Corpora Internet) dataset on November 29, 2023. Building on this foundation, we continue to expand the data source, adopt stricter data cleaning methods, and complete the construction of the CCI 3.0 dataset. This dataset is composed of high-quality, reliable Internet data from trusted sources. And then with more stricter filtering, The CCI 3.0 HQ corpus released is about 500GB in size.

Introducing paper: CCI3.0-HQ: a large-scale Chinese dataset of high quality designed for pre-training large language models
Infinity-MM

We collect, organize and open-source the large-scale multimodal instruction dataset, Infinity-MM, consisting of tens of millions of samples. Through quality filtering and deduplication, the dataset has high quality and diversity. We propose a synthetic data generation method based on open-source models and labeling system, using detailed image annotations and diverse question generation.

Introducing paper: Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data
ColonINST-v1

ColonINST is a large-scale instruction tuning dataset designed for multimodal analysis in colonoscopy. This dataset comprises 62 categories, and 303,001 colonoscopy images, including 128,620 positive and 174,381 negative cases collected from 19 publicly available datasets. We enhanced 128,620 colonoscopy images with detailed captions using a pipeline that interacts with GPT-4V through custom prompts, enriching the dataset for AI model training. We finally restructured 450,724 visual dialogues to guide the AI model through four downstream tasks critical for multimodal medical AI applications.

Please cite our work if you like it!
```text
@article{ji2024frontiers
  author = {Ji, Ge-Peng and Liu, Jingyi and Xu, Peng and Barnes, Nick and Khan, Fahad Shahbaz and Khan, Salman and Fan, Deng-Ping},
  title = {Frontiers in Intelligent Colonoscopy},
  journal = {arXiv preprint arXiv:2410.17241},
  year = {2024}
}
```

Introducing paper: Frontiers in Intelligent Colonoscopy
ColonINST-v1 (Seen)

ColonINST is a large-scale instruction tuning dataset designed for multimodal analysis in colonoscopy. This dataset comprises 62 categories, 303,001 colonoscopy images, including 128,620 positive and 174,381 negative cases collected from 19 publicly available datasets.  We enhanced 128,620 colonoscopy images with detailed captions using a pipeline that interacts with GPT-4V through custom prompts, enriching the dataset for AI model training.   We finally restructured 450,724 visual dialogues to guide the AI model through four downstream tasks critical for mutlimodal medical AI applications.
ColonINST-v1 (Unseen)

ColonINST is a large-scale instruction tuning dataset designed for multimodal analysis in colonoscopy. This dataset comprises 62 categories, 303,001 colonoscopy images, including 128,620 positive and 174,381 negative cases collected from 19 publicly available datasets. We enhanced 128,620 colonoscopy images with detailed captions using a pipeline that interacts with GPT-4V through custom prompts, enriching the dataset for AI model training. We finally restructured 450,724 visual dialogues to guide the AI model through four downstream tasks critical for multimodal medical AI applications.
Bengali Social Media Depressive Dataset (BSMDD)
Full name: Bengali Social Media Depressive Dataset (BSMDD)

Our dataset, BSMDD, was collected from various open social media platforms and translated and annotated by native Bengali speakers with expertise in both language and mental health. It contains 21,910 cleaned samples, including 10,961 labeled as Depressed and 10,949 as Non-Depressed. The dataset is publicly accessible, providing a valuable resource for further research in depression detection in Bengali social media content. The expert annotation process, conducted by professionals, ensures high validity, making BSMDD particularly important for advancing mental health research through social media analysis. This dataset is also published on Mendeley. 

Mendeley: https://data.mendeley.com/datasets/pdtyb7df6z/1

ResearchGate: https://www.researchgate.net/publication/384660753_Bengali_Social_Media_Depressive_Dataset_BSMDD
TextKVQA

Text present in images are not merely strings, they provide useful cues about the image. Despite their utility in better image understanding, scene texts are not used in traditional visual question answering (VQA) models. In this work, we present a VQA model which can read scene texts and perform reasoning on a knowledge graph to arrive at an accurate answer. Our proposed model has three mutually interacting modules: (i) proposal module to get word and visual content proposals from the image, (ii) fusion module to fuse these proposals, question and knowledge base to mine relevant facts, and represent these facts as multi-relational graph, (iii) reasoning module to perform a novel gated graph neural network based reasoning on this graph.

The performance of our knowledge-enabled VQA model is evaluated on our newly introduced dataset, viz. textKVQA. To the best of our knowledge, this is the first dataset which identifies the need for bridging text recognition with knowledge graph based reasoning. Through extensive experiments, we show that our proposed method outperforms traditional VQA as well as question-answering over knowledge base-based methods on text-KVQA