datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
OCRBenchGithub|Paper
OCRBench has been accepted by Science China Information Sciences.
NeMo
NVIDIA NeMo Speech
Checkout our HuggingFace🤗 collection for the latest open
weight checkpoints and demos!
Updates
2026-03: Nemotron 3 VoiceChatis now released in Early Access. Built on the Nemotron Nano v2 LLM backbone with Nemotron speech and TTS decoder, VoiceChat delivers full-duplex, natural, interruptible conversations with low latency. Try out the demo and apply for early access.
2026-03: Nemotron-Speech-Streaming v2603 has been
updated. It has been… See the full description on the dataset page: https://huggingface.co/datasets/echodict/NeMo.MM-SafetyBench-plus-plus
MM-SafetyBench++
Project Page | Paper | Code
MM-SafetyBench++ is a benchmark designed for evaluating contextual safety in Multi-Modal Large Language Models (MLLMs). It challenges models to distinguish subtle contextual differences between scenarios that may appear visually or textually similar but diverge significantly in safety intent.
Dataset Summary
For each unsafe image-text pair, the benchmark includes a corresponding safe counterpart created through minimal… See the full description on the dataset page: https://huggingface.co/datasets/EchoSafe-MLLM/MM-SafetyBench-plus-plus.mushroom剧毒鹅膏的典型特征是蘑菇根部是球状，菌盖没有条纹。菌盖周边有条纹的鹅膏基本都是低毒或者无毒品种
菌褶是什么
**菌褶就是蘑菇菌盖翻开底面那一条条放射状的"薄片"**，像扇骨或者雨伞骨架那样从菌柄中心往外辐射排列。
它干嘛用的
菌褶是蘑菇的"产孢器官"——孢子就长在菌褶的两个侧面上。你可以把菌褶理解为"增加表面积用的"，薄片一折一叠，单位体积里能挂的孢子就多，风一吹或者雨一滴，孢子就散出去繁殖。
为什么识菇要看菌褶
菌褶的这几个特征，是区分蘑菇种类的关键：
颜色：白、粉、黄、褐，还有大青褶伞那种成熟变绿的——这是最显眼的鉴别点
密度：稀 / 中 / 密，数一数每厘米几条
着生方式：
离生：菌褶不和菌柄连，留一圈空隙（大青褶伞就是这种）
弯生 / 直生：稍微贴一点
延生：菌褶顺着菌柄往下爬
等长 / 不等长：有没有插短褶
一个直观的比喻
把蘑菇翻过来，菌盖底下一把"百叶窗"或者"折扇"——那就是菌褶。掰一小片下来放大看，侧面像手风琴风箱那样一皱一皱的，孢子就在那皱褶表面长出来。… See the full description on the dataset page: https://huggingface.co/datasets/echodict/mushroom.Echo-4o-Image
Echo-4o-Image Dataset
Paper | Project Page | Code
Introduction
Echo-4o-Image is a 180K-scale synthetic dataset generated by GPT-4o, designed to advance open-source models in image generation. While real-world image datasets are valuable, synthetic images offer crucial advantages, especially in addressing blind spots in real-world coverage:
Complementing Rare Scenarios: Synthetic data can generate examples for scenarios less represented in real-world datasets, such as… See the full description on the dataset page: https://huggingface.co/datasets/Yejy53/Echo-4o-Image.Echo-4o-Image-Surrel-Fantasyppv5echo4o_2kpaper_visionbananahomotopyTypeTheoryThis is a textbook on informal homotopy type theory.
It is part of the Univalent foundations of mathematics
project that took place at the Institute for Advanced Study in 2012/13.
License
This work is licensed under the
Creative Commons Attribution-ShareAlike 3.0 Unported License.
Distribution
Compiled and printed versions of the book are available at the
homotopy type theory website,
and nightly builds are available on the
github wiki.
Editing the… See the full description on the dataset page: https://huggingface.co/datasets/echodict/homotopyTypeTheory.typst_shztypst_jpmHLE_mathfer2013_preprocessbrutalist_echo_infra
brutalist_echo_infra
Generated with Autobatch Generator, a free browser-based tool for building synthetic AI image datasets.
Images: 161
Generated: 2026-08-25
See manifest.csv in this repo for the prompt, seed, model and dimensions used for each image.
ElliptiGOhttps://uuyc.163.com/help/20250919/40220_1260166.html 网易UU远程开机小米路由器如何设置插件?
SS-7i-feature-leantosteer.jpg StreetStrider_7i 椭圆自行车的实物图，分析它身体倾斜转向系统”（Lean-to-Steer System）的实现，那两根细连杆的作用是什么
从这张 StreetStrider_7i 的前端实物图看，它的“身体倾斜转向（Lean-to-Steer）”本质上是把“车架相对前桥的侧倾/扭转”通过一套连杆机构，转换成“两前轮的转角”。画面里你问的“两根细连杆”，就是左右各一根的转向拉杆（tie rods），作用非常关键。
倾斜如何变成转向（按这张图能读出的机构逻辑）
前桥两侧各有一个转向节/转向臂（靠近轮毂处的小摇臂）。
车架中间有一个转向摇臂/摆臂（在两根细连杆的内侧端汇到一起的那个铰点附近）。
当骑行者向左/右“压身”让车架发生侧倾时，车架上的中间摇臂相对前桥产生偏转：… See the full description on the dataset page: https://huggingface.co/datasets/echodict/ElliptiGO.Detailed_CaptionGithub|Paper
cocot
CoCoT: Collaborative Cross-modal Chain-of-Thought Dataset
This repository contains the complete CoCoT (Collaborative Cross-modal Chain-of-Thought) dataset, including bounding box annotations and reasoning chains for complex visual question answering tasks.
Associated Paper: Watch Wider and Think Deeper: Collaborative Cross-modal Chain-of-Thought for Complex Visual Reasoning; Accepted to: NeurIPS 2026 Workshop; Authors: Wenting Lu, Didi Zhu, Tao Shen, Donglin Zhu, Ayong Ye, Chao Wu… See the full description on the dataset page: https://huggingface.co/datasets/echo-deer/cocot.EchoVLM-DatasetIt is very unfortunate that only a small number of public datasets have been granted open source licenses.
EchoVLM Ultrasound VQA Dataset
📋 Overview
This dataset is curated for Visual Question Answering (VQA) tasks in the ultrasound medical imaging domain. It supports both Chinese (zh) and English (en) language annotations, facilitating multilingual research and development of vision-language models for universal ultrasound intelligence.
Due to the specialized… See the full description on the dataset page: https://huggingface.co/datasets/chaoyinshe/EchoVLM-Dataset.cat-cat-dataecho-4o-instruction-followingecho2025-mitechoA few images of Echo
echo_testing
Dataset Card for "echo_testing"
More Information needed
mollyImage-Based-Virtual-Try-On-A-Survey-Test-Resultsimgbed3_Phases_AugPseudo-echo3_Phases_Aug_zip