datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
tess-agentnet
TESS AgentNet Dataset
Computer use trajectories for training Vision-Language-Action models.
Features
image: Screenshot (PIL Image)
instruction: Task description
action_type: 0=MOUSE, 1=KEYBOARD
mouse_x, mouse_y: Normalized coordinates [0,1]
click_type: 0-8 (NO_CLICK, LEFT_CLICK, etc.)
keyboard_text: Text with special tokens
os_type: ubuntu, windows_macos
episode_id, step_idx: Episode structure
Click Types
Index
Type
Description
0
NO_CLICK… See the full description on the dataset page: https://huggingface.co/datasets/TESS-Computer/tess-agentnet.syntheticDocQA_government_reports_test_tesseracttess-lightcurves-planets
TESS Light Curves for Planet Detection
A curated set of TESS light curves with labels for training transit detection models.
Each star in this dataset either has a reported planet candidate, a confirmed planet,
a false positive, or a pipeline detection from the public NASA/TESS catalogs.
What's inside
7 607 stars (TIC targets)
13 254 catalog signals (TOI / CTOI / TCE)
31 345 light curve FITS files
This dataset mixes three different things. Keep them separate in your… See the full description on the dataset page: https://huggingface.co/datasets/saadtaleb/tess-lightcurves-planets.tessera_sample
Tessera Sample Dataset
This dataset is a small sample of the Tessera dataset developed by the University of Cambridge Earth Observation Group.This sample is intended for testing workflows, experimentation, and demonstration purposes.
Original Tessera dataset: GitHub Repository
Dataset Overview
TESSERA (Temporal Embeddings of Surface Spectra for Earth Representation and Analysis) encodes spatio-temporal information from Earth observation data into compact embeddings.This… See the full description on the dataset page: https://huggingface.co/datasets/torchgeo/tessera_sample.docvqa_test_subsampled_tesseractarxivqa_test_subsampled_tesseracttesst6This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.0",
"robot_type": "my_robot",
"total_episodes": 2,
"total_frames": 35,
"total_tasks": 2,
"total_videos": 0,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 10,
"splits": {
"train": "0:2"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/stevenoh2003/tesst6.syntheticDocQA_energy_test_tesseracttatdqa_test_tesseractinfovqa_test_subsampled_tesseracttabfquad_test_subsampled_tesseracttessera-quantization-research-evidence
Tessera Quantization Research Evidence
This dataset is the primary-source measurement evidence from an ongoing research
program studying calibrated low-bit quantization (ternary, int4, vector-quantized
codebooks) for LLM inference on heterogeneous AMD hardware (RDNA3 iGPU, XDNA1/2
NPU, Zen 4/5 CPU). The work is done in a fork of llama.cpp (project name
"Tessera") that adds calibrated per-tensor ternary/payload4/VQ quantization,
NPU offload, and RDNA3-native GPU kernels.
This is… See the full description on the dataset page: https://huggingface.co/datasets/Tribunus-dev/tessera-quantization-research-evidence.syntheticDocQA_artificial_intelligence_test_tesseractquickdraw-circles
Quick, Draw! Circles - Trajectory Dataset
Dataset for training trajectory prediction models, specifically designed for the Qwen-DiT-Draw project.
Dataset Description
This dataset contains chunked trajectory data from the Quick, Draw! circle category, formatted for training diffusion-based trajectory prediction models.
Key Features
Variable-length trajectories with stop signals (GR00T-style)
16-point chunks with (x, y, state) format
Loss masking for handling… See the full description on the dataset page: https://huggingface.co/datasets/TESS-Computer/quickdraw-circles.shiftproject_test_tesseractneopets-packsyntheticDocQA_healthcare_industry_test_tesseractquickdraw-circles-delta
Quick, Draw! Circles - Trajectory Dataset
Dataset for training trajectory prediction models, specifically designed for the Qwen-DiT-Draw project.
Dataset Description
This dataset contains chunked trajectory data from the Quick, Draw! circle category, formatted for training diffusion-based trajectory prediction models.
Key Features
Variable-length trajectories with stop signals (GR00T-style)
16-point chunks with (x, y, state) format
Loss masking for handling… See the full description on the dataset page: https://huggingface.co/datasets/TESS-Computer/quickdraw-circles-delta.tessellation-patternsqari-0.1-tesseract-ocr-eval-resultsmanual-collect-tessellation-augmentdb_tessatesst001opensource-collect-tessellation-augmenttesseract_ros2tesseract_rospontus-tessera-projecttesst113my_first_lora_v1-dataset