Omkar2118/datasets huggingface dataset:
SSBI Dataset
Full name: Synthetic Signature Bankcheck Images

The Synthetic Signature Bankcheck Images (SSBI) Dataset is the first publicly available dataset of bank check images with annotations for detecting handwritten components, including names, amounts, dates, and signatures. It also supports both writer-independent and writer-dependent signature verification tasks by providing labels for genuine and forged signatures and IDs of the signature authors.

This dataset contains 2180 synthetic bank check images with:
-1680 genuine signatures
- 500 forged signatures

The signatures are from 19 different signers. In addition to the signature bounding boxes, the dataset also contains the bounding boxes for the date, amount, and payee fields.

Introducing paper: Enhanced Bank Check Security: Introducing a Novel Dataset and Transformer-Based Approach for Detection and Verification
ShareGPT4Video

The **ShareGPT4Video** dataset is a large-scale resource designed to improve video understanding and generation¹. It features **1.2 million highly descriptive captions**⁴ for video clips, surpassing existing datasets in diversity and information content⁴. The captions cover a wide range of aspects, including world knowledge, object properties, spatial relationships, and aesthetic evaluations⁴.

The dataset includes detailed captions of **40K videos** generated by **GPT-4V**¹ and **4.8M videos** generated by **ShareCaptioner-Video**¹. The videos are sourced from YouTube and other user-uploaded video websites, and they cover a variety of scenarios, such as human activities and auto-driving¹.

The ShareGPT4Video dataset also provides a basis for the **ShareCaptioner-Video**, an exceptional video captioner capable of efficiently generating high-quality captions for videos of a wide range of resolution, aspect ratio, and duration¹.

For example, the dataset includes a detailed caption of a video documenting a meticulous meal preparation by an individual with tattooed forearms¹. The caption describes the individual's actions in detail, from slicing a cucumber to mixing the dressing and adding croutons to the salad¹.

In addition to its use in research, the ShareGPT4Video dataset has been used to train the **sharegpt4video-8b** model, an open-source video chatbot². This model was trained on open-source video instruction data and is primarily intended for researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence².

(1) arXiv:2406.04325v1 [cs.CV] 6 Jun 2024. https://arxiv.org/pdf/2406.04325.
(2) ShareGPT4V: Improving Large Multi-Modal Models with Better Captions. https://arxiv.org/abs/2311.12793.
(3) Lin-Chen/sharegpt4video-8b · Hugging Face. https://huggingface.co/Lin-Chen/sharegpt4video-8b.
(4) ShareGPT4Video: Improving Video Understanding and Generation with .... https://www.aimodels.fyi/papers/arxiv/sharegpt4video-improving-video-understanding-generation-better-captions.
(5) GitHub - ShareGPT4Omni/ShareGPT4Video: An official implementation of .... https://github.com/ShareGPT4Omni/ShareGPT4Video.
(6) undefined. https://sharegpt4video.github.io/.

Introducing paper: ShareGPT4Video: Improving Video Understanding and Generation with Better Captions
Arena-Hard-Auto

The **Arena-Hard-Auto** benchmark is an automatic evaluation tool for instruction-tuned Language Learning Models (LLMs)¹. It was developed to provide a cheaper and faster approximation to human preference¹.

Here are some key features of the Arena-Hard-Auto benchmark:
- It contains 500 challenging user queries¹.
- It uses GPT-4-Turbo as a judge to compare the models' responses against a baseline model (default: GPT-4-0314)¹.
- It employs an automatic judge as a cheaper and faster approximator to human preference¹.
- It has the highest correlation and separability to Chatbot Arena among popular open-ended LLM benchmarks¹.
- If you are curious to see how well your model might perform on Chatbot Arena, Arena-Hard-Auto is recommended¹.

The benchmark is built from live data in Chatbot Arena, which is a popular crowd-sourced platform for LLM evaluations⁴. It offers significantly stronger separability against other benchmarks with tighter confidence intervals². 

(1) GitHub - lm-sys/arena-hard-auto: Arena-Hard-Auto: An automatic LLM .... https://github.com/lm-sys/arena-hard-auto.
(2) Arena Hard – UC Berkeley Sky Computing. https://sky.cs.berkeley.edu/project/arena-hard/.
(3) From Live Data to High-Quality Benchmarks: The Arena-Hard Pipeline .... https://lmsys.org/blog/2024-04-19-arena-hard/.
(4) GitHub - lm-sys/arena-hard-auto: Arena-Hard-Auto: An automatic LLM .... https://github.com/lm-sys/arena-hard-auto.
(5) Arena-Hard：开源高质量大模型评估基准-CSDN博客. https://blog.csdn.net/weixin_57291105/article/details/138132998.

Introducing paper: From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline
ModelNet40 (Pretrained on ShapeNet)

The ModelNet40 zero-shot 3D classification performance of models pretrained on ShapeNet only.

Introducing paper: ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding