AI Training Datasets

Curated multimodal vision dataset of 1-minute candlestick chart captures with technical indicators and verified trade outcomes (WIN / LOSS). This dataset strictly adheres to the official Hugging Face ImageFolder standard: - train/metadata.jsonl: Single-format JSONL metadata with sample annotations - train/images/.jpg: Candlestick chart captures

Dataset

travel-pointcloud-text-benchmark

Hanna C. Lewandowski

This repository contains a preparation pipeline and a small metadata sample for Travel work with Pointcloud Text inputs. It does not claim to be a complete benchmark release; the loader documents how source data is normalized and validated. - loaddata.py — loading, cleaning, and split preparation code. - datasetinfos.json — schema and split metadata. - metadatasample.jsonl — small, human-readable records for checking the schema. - README.md — data card and usage notes. Before using the prepared data, verify source licenses, duplicates across splits, missing values, label balance, and modality-specific corruption. Record the source version and every filtering rule so a later run can…

Dataset · Image classification

tree_species_classification_slovak_normal_cropped

AgML

This dataset provides real RGB images of tree species collected in natural field environments across Slovakia and the Czech Republic. Images were captured using handheld cameras (Sony Alpha 7 and Canon EOS 4000D) during field surveys in June and August 2022. The dataset contains 1,367 images across 4 classes: European beech, European silver fir, Norway spruce, Sessile oak. This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. This dataset was reformatted from its original format to match HuggingFace standards.

Trump speak dataset augmented with uwu-style reasoning traces generated by lfm2.5-2.6b

Independent yaw 10/20/30-degree conditions, original agentview, robot-base yaw compensation and action rotation enabled. MuJoCo 3.2.3. Each buffer has 400 source-policy rollouts across 40 tasks and initial states 0-9, retaining both outcomes. Prompt-only adaptation uses 1,000 steps, batch 128, AdamW LR 1e-5. Source SG-LP training used 20,000 steps at LR 1e-5. Dataset NPZs contain normalized observation/state frames and success metadata, with no expert or predicted-action imitation targets. Normalization is the source policy's fixed 8-D state / 7-D action quantile statistics. Models include full source/adapted weights, source normalization, prompt resume states, protocol/configuration, and…

Dataset · Speech recognition

unnamed-ar

Ahmed Wasfy

The data/ar/ partition of espnet/yodas3, repackaged as parquet with the audio inline so it loads without a script: Every row is one long-form recording (median ~5 minutes) with its full transcript. Audio container only. Upstream ships Opus in a WebM/Matroska container, which libsndfile cannot open. Here it is Opus in Ogg, remuxed with ffmpeg -c:a copy — the Opus packets are copied untouched, so the audio is bit-identical to upstream. 48 kHz. Metadata joined. Upstream keeps audio in data/ar/audio/.tar and the 12 metadata columns in data/ar/metadata/.parquet; this version carries both in one row. Dialect columns added (see below). Nothing was filtered, resampled, re-encoded, segmented, or…

20,000 previously public ranked games collected from the top 20 teams. Replay bytes are unchanged; team and bot labels already present in public gameplay remain present. This collection does not contain private experimental matches, source archives, account responses, credentials or collection indexes. Each TAR contains gzip-compressed.replay files. Extract an archive with tar -xf replays/shard-0000.tar. For the Battlecode visualiser, decompress a replay to a separate file: gzip -dc replays/GAMEID.replay > view.replay, then open view.replay at https://game.battlecode.au/visualiser. manifest.json records archive sizes, SHA256 hashes and game counts. Archive names are unique across the…

This dataset was created using LeRobot. Part B (a white disc with a hub and a bar) is found with the wrist camera, picked from the plate and seated into fixture A. Universal Robots UR7e, Robotiq gripper, wrist-mounted Intel RealSense D405. Collected autonomously during a continuous 48-hour run that started on 2026-10-10, to cover the lighting changes of day and night. This repository is one part of that run: 101 episodes, 65387 frames at 30 fps. - State and action (16 values each): 6 joint angles, TCP position, TCP orientation (6D), gripper position. Also recorded: joint velocity, torque and current, TCP force, raw force/torque wrench, gripper effort, capture time. - The wrist camera is…

This dataset was created using LeRobot. Part B (a white disc with a hub and a bar) is found with the wrist camera, picked from the plate and seated into fixture A. Universal Robots UR7e, Robotiq gripper, wrist-mounted Intel RealSense D405. Collected autonomously during a continuous 48-hour run that started on 2026-10-10, to cover the lighting changes of day and night. This repository is one part of that run: 100 episodes, 64553 frames at 30 fps. - State and action (16 values each): 6 joint angles, TCP position, TCP orientation (6D), gripper position. Also recorded: joint velocity, torque and current, TCP force, raw force/torque wrench, gripper effort, capture time. - The wrist camera is…

Data and results for Multi-Agent Prompting with In-Context Feedback for Cross-Modal Urban Event Chain Prediction (Sensors, MDPI). Code: github.com/bibekgupta3333/urban-event-chain-prediction-multi-agent-prompting The nine -bedrock- run directories are the model grid the paper reports: 5 configurations (singleagent, multiagent, noisysingle, multifeedback, multinofeedback) × expert prompt level × 3 cities, 85 test chains per configuration. Bedrock exposes no seed, so these runs are not byte-reproducible. - Traffic data are municipal traffic-speed feeds (probe-derived segment estimates, daily) — Chicago Traffic Tracker (sxs8-h27x), NYC Real-Time Traffic Speed (i4gi-tjb9), SF via DataSF. They…

Cached inputs for UCPE-conditioned Cosmos student distillation, with a short 33-frame GT seed and causal 81-frame target clips. Contains 9,188 DL3DV and 7,993 RealEstate10K scenes, 54,643 target clips, and a fixed 5+5-scene validation split. The bundle contains video VAE latents, rendered GS VAE features/masks, metric camera trajectories/intrinsics, projected text contexts, the matching projection, and provenance. Raw RGB videos and model weights are not included. Read shortseed33-v1-20261007/index.json and require complete: true and all 120 shards. A repository containing some shards is not a completed dataset. The full extracted payload is 499,974,383,360 bytes (~500 GB / 465.64 GiB)…

Dataset · Text to speech

uz-speech-sidon-24k

Aslkhon Khoshimkhujaev

Узбекская речь для обучения TTS: ~690 часов, 24 kHz, с транскриптами. Две версии аудио с одинаковыми id. Доступ — по заявке, одобряется вручную. Подробное описание — в DETAILS.md (доступно после одобрения).

Model-assisted research draft. The dataset contains 20,000 selected items from Telegram and YouTube, with original text/captions and selected original recording files. Labels have not received independent human validation. All acceptance, reuse and review flags retain their recorded values. Each sentiment has 5,000 items: positive, neutral, negative, and stronglynegative. Sources cover 200 Telegram channels, 200 public Telegram groups, and 57 YouTube channels. This is a selected, balanced sample; these counts do not estimate sentiment prevalence in the source population. The language labels are uzLatn (6,000), uzCyrl (2,388), uzrumixed (3,000), ru (4,000), spoken uz (4,541), en (65), and…