AI and ML
Datasets commonly used by Artificial Intelligence and other Machine Learning
AlpacaFarm
infoAlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback
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/datasets/ai/alpaca-farmaudioset
infoAudioSet is an ontology and human-labeled dataset for audio event detection. It consists of 2,084,320 ten-second sound clips from YouTube videos labeled with a hierarchical ontology of 632 audio event classes, including human and animal sounds, musical instruments, and everyday environmental noises.
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/datasets/ai/audiosetbiomed_clip
infoBiomedCLIP is a biomedical vision-language foundation model that is pretrained on PMC-15M, a dataset of 15 million figure-caption pairs extracted from biomedical research articles in PubMed Central, using contrastive learning
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/datasets/ai/biomed-clipblip_2
infoBLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
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/datasets/ai/blipblip_2
infoBLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
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/datasets/ai/blip2coco
infoCOCO is a large-scale object detection, segmentation, and captioning dataset
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/datasets/ai/cocoCode Llama
DeepAccident
infoDeepAccident is the first V2X (vehicle-to-everything simulation) autonomous driving dataset that contains diverse collision accidents that commonly occur in real-world driving scenarios
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/datasets/ai/deep-accidentDeepSeek
infoDeepSeek trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrates remarkable reasoning capabilities. Through RL, DeepSeek-R1-Zero naturally emerges with numerous powerful and intriguing reasoning behaviors.
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/datasets/ai/deepseekDINO v2
infoDINOv2 is a self-supervised method to learn visual representation
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/datasets/ai/dinov2epic-kitchens
infoEpic-Kitchens-100 is a large-scale dataset in first-person (egocentric) vision; multi-faceted, audio-visual, non-scripted recordings in kitchen environments.
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/datasets/ai/epic-kitchensflorence
infoFlorence-2 is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks.
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/datasets/ai/florencegemma
infoGemma is a family of lightweight, state-of-the art open models built from the research and technology used to create Gemini models
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/datasets/ai/gemmagpt
infoLanguage models are unsupervised multitask learners
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/datasets/ai/gptgte-Qwen2
infogte-Qwen2-7B-instruct is the latest model in the gte (General Text Embedding) model family that ranks No.1 in both English and Chinese evaluations on the Massive Text Embedding Benchmark MTEB benchmark (as of June 16, 2024).
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/datasets/ai/alibabaibm-granite
infoGranite 3.0, a new set of lightweight, state-of-the-art, open foundation models ranging in scale from 400 million to 8 billion active parameters
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/datasets/ai/ibm-graniteIdefics2
infoIdefics2 is an open multimodal model that accepts arbitrary sequences of image and text inputs
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/datasets/ai/idefics2Imagenet 1K
inaturalist
infoThe iNaturalist 2017 dataset (iNat) contains 675,170 training and validation images from 5,089 natural fine-grained categories
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/datasets/ai/inaturalistinfly
infoINF-Retriever-v1 is an LLM-based dense retrieval model developed by INF TECH. It is built upon the gte-Qwen2-7B-instruct model and specifically fine-tuned to excel in retrieval tasks, particularly for Chinese and English data
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/datasets/ai/inflyinstruct-blip
infoInstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
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/datasets/ai/instruct-blipintfloat
infoA novel and simple method for obtaining high-quality text embeddings using only synthetic data and less than 1k training steps
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/datasets/ai/intfloatLAION
linq
infoLinq-Embed-Mistral has been developed by building upon the foundations of the E5-mistral-7b-instruct and Mistral-7B-v0.1 models
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/datasets/ai/intfloatllama
infoLLaMA, a collection of foundation language models ranging from 7B to 65B parameters
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/datasets/ai/llamaLlama2
llama3
infoLlama 3 is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage
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/datasets/ai/llama3llama4
infoLlama 4, developed by Meta, introduces a new auto-regressive Mixture-of-Experts (MoE) architecture
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/datasets/ai/llama4/Llava_OneVision
Lumina
infoLumina-Image 2.0: A Unified and Efficient Image Generative Framework
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/datasets/ai/luminamixtral
msmarco
infoThe MS MARCO dataset is a large-scale information retrieval benchmark that uses real-world questions from Bing’s search queries to evaluate the performance of machine learning models in generating answers
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/datasets/ai/msmarconatural-questions
infoNatural Questions corpus, a question answering data set. Questions consist of real anonymized, aggregated queries issued to the Google search engine
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/datasets/ai/natural-questionsobjaverse
infoObjaverse is a Massive Dataset with 800K+ Annotated 3D Objects
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/datasets/ai/objaverseopenai-whisper
infoWhisper is a state-of-the-art model for automatic speech recognition (ASR) and speech translation
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/datasets/ai/whisperpythia
infoPythia is the first LLM suite designed specifically to enable scientific research on LLMs
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/datasets/ai/pythiaqwen
infoQwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model
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/datasets/ai/qwenR1-1776
rag-sequence-nq
infoRAG models where the parametric memory is a pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a pre-trained neural retriever
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/datasets/ai/rag-sequence-nqred-pajama-v2
infoRedPajama-V2, a massive web-only dataset consisting of raw, unfiltered text data together with quality signals and metadata
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/datasets/ai/red-pajama-v2s1-32B
infos1 is a reasoning model finetuned from Qwen2.5-32B-Instruct on just 1,000 examples. It matches o1-preview & exhibits test-time scaling via budget forcing.
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/datasets/ai/simplescalingsatlas_pretrain
infoSatlasPretrain, a remote sensing dataset that is large in both breadth and scale, combining Sentinel-2 and NAIP images with 302M labels under 137 categories and seven label types
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/datasets/ai/allenaiscalabilityai
infoA novel transformer-based architecture for text-to-image generation that uses separate weights for the two modalities and enables a bidirectional flow of information between image and text tokens
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/datasets/ai/stabilityai/SlimPajama
infoSlimPajama is a rigorously deduplicated, multi-source dataset, which has been refined and further deduplicated to 627B tokens from the extensive 1.2T token RedPajama dataset contributed by Together
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/datasets/ai/slim-pajamat5
infoThe T5 model, short for Text-to-Text Transfer Transformer, is a machine learning model developed by Google
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/datasets/ai/t5Tulu
infoTülu 3: Pushing Frontiers in Open Language Model Post-Training
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/datasets/ai/tuluV2X
infoV2X-Sim, a comprehensive simulated multi-agent perception dataset for V2X-aided autonomous driving
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/datasets/ai/v2xvideo-MAE
infoVideo masked autoencoder (VideoMAE) is a scalable and general self-supervised pre-trainer for building video foundation models
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/datasets/ai/opengvlabvideoMAE-v2
infoVideo masked autoencoder (VideoMAE) is a scalable and general self-supervised pre-trainer for building video foundation models
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/datasets/ai/opengvlabvit
infoThe Vision Transformer (ViT) model uses the transformer architecture to process image patches for tasks like image classification
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/datasets/ai/vitwildchat
infoWildChat, a corpus of 1 million user-ChatGPT conversations, which consists of over 2.5 million interaction turns
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/datasets/ai/wildchat