This ledger qualifies 5 readings into 3 daily berth records totaling 16 sample-volume units. There are 2 daily records with more than one reading.
SAVRN Model Hub
AI Training Datasets
Each dataset with its card, its structure (every split, row count and column type), its files, its license, and the models that disclose training on it.
11,122Models
3,164Datasets
285Papers
6,792Publishers
11,852Sourced relationships
Updated 2026-10-18 · How the library is built
3,164 datasets, sorted by most downloaded.
is a large-scale, fully synthetic, ground-truth-complete benchmark for the two classical sensor-calibration problems of robotics, hand-eye calibration, A X = X B, and simultaneous robot-world / hand-eye calibration, A X = Y B, together with their spatio-temporal extension (unknown clock offset between the robot and the camera). Instead of images, every sequence consists of two time-stamped pose streams, the robot flange trajectory (from forward kinematics of real manipulator models) and the pose of the observed reference in the camera, both as exact ground truth and as measurements corrupted by physically motivated noise models. This isolates the numerical calibration problem from image…
L-CIRR is a benchmark for composed image retrieval (CIR) with long-form modification texts. It extends the CIRR dataset by replacing each short, single-sentence modification text with a long, detailed, multi-sentence description generated by a vision-language model (Gemini-2.5-Flash), while preserving the original image pairs and benchmark protocol. This dataset accompanies the paper "Beyond Single Sentences: Composed Image Retrieval with Long-Form Modification Texts" (ACCV 2026). Existing CIR benchmarks provide only brief, often ambiguous modification texts that capture a small subset of the visual differences between the reference and target images, giving weak supervision for learning…
This is a new research campaign from the pinned public A100 artifact revision 45671fe9ea2bd97491d921338cbf85a853e34578, not a continuation of the lost fresh-RTX4090 2k checkpoints. The origin teacher cache and base model are referenced by revision/SHA rather than copied here. New measurements and any new checkpoints/derived tensors will be uploaded by stage. Until verified result folders appear, this repository contains only the plan, not completed experiment results.
LIBERO-Pro2.0 provides robot manipulation demonstrations for imitation learning and vision-language-action research. It contains 40,470 stored demonstrations across 32 base tasks in four LIBERO suites, including base tasks and perturbation variants. Each demonstration includes a language instruction, scene and wrist camera observations, robot states, and actions. Trajectories are generated by a privileged-state oracle and distributed in native HDF5 format. - 8,258,504 steps, with two 128 × 128 RGB camera views and 7-dimensional actions. - 6,745 review videos: five successful examples per task/variation instance. - Training data only; no validation or test split is provided. Repeated…
LIFT-Vista is a dataset with large camera viewpoint changes and joint camera-layout annotations. Every clip has 81 frames at 16 fps (about 5 s) at the native resolution of its source video (98% are 1280×720), encoded with H.264. The release is about 413 GB, of which 407 GB are videos. Videos and annotations are stored as tar shards in tars/ (about 4 GB per video shard). Extracting them in the dataset root restores the videos/ and annotations/ directories that the CSV paths point to (413 GB in total): camlayout.csv only needs the camlayout shards (179 GB): is SpatialVID, Sekai or RealEstate10K, is the id of the video in the source dataset, and is the window index w of the 81-frame window…
LMBuild LMBuild asks a system to build a real, working object as an assembly of parts. The object can be an office desk, a wheelchair, a scissor car jack, a 3D printer or a LEGO fire engine. The system places catalogue parts or creates new ones, and declares what each part is, what it is made of, where the object moves and how it is put together. It works from the object's name and an in-the-wild photo, through a tool environment. The result is then scored as a physical product
This repo holds every shape of the released dataset described under "The data", packed as tar shards per representation. Each split's uuids are sorted and cut into shards of 1,000 shapes. A tar holds one representation's files for one shard (atlas: atlas.npz; voxels: the two.vxz files; multiview: multiview/; thumbnail: thumbnail.png) at paths /, as in the tree under "The data". Shard k holds the same shapes in every representation. The atlas.npz files here are stored compressed; the arrays are the same and np.load reads them the same way. Emissive 3D shapes from TexVerse with their material and emission maps in three representations: a UV atlas, sparse voxels, and six rendered views. One…
A supervised training dataset for machine learning and AI systems that learn to identify lithology from well-log data. This dataset contains exactly 400 wells with standardized wireline-log measurements and corresponding lithological labels. https://huggingface.co/datasets/NoraResearchLab/Lithology-Training-Dataset This dataset is intended for training and model development. It is separate from the independent evaluation benchmark. - SHALE - SANDSTONE - LIMESTONE - DOLOMITE - ANHYDRITE - SALT - COAL - UNKNOWN/MIXED UNKNOWN/MIXED represents intervals where the available log evidence does not provide sufficient confidence for assigning one of the principal lithological classes. Depth-indexed…
Korean → English translation benchmark built from Korean live-streaming chat messages. Each row pairs a Korean chat message with an English reference translation. Where a message relies on streaming slang, community jargon, or a streamer/game name, a short background note explains the term; it is an empty string when no note is needed. - 1,000 sentence pairs (test split) - 728 rows with a non-empty background, 272 rows with ""
This dataset contains Tigrigna retrieval documents used for MEIV experiments.
1,801 omni-view questions over 32 synthetic workplace projects. This is the portable evaluation corpus, not the original source-paper collection. PDF/PPT content is supplied as rendered page images; native Slack/Notion messages remain text. No OCR or generated image captions are supplied to readers. Pin a Hub commit SHA with revision=" " for reproducible experiments. The QA dataset contains question, answer, task, reviewed evidence, solution and annotationjson with the full review record. Gold and solution are evaluation Download all actual images and the corpus for evaluation: corpus/units.json has repository-relative image paths. Every image is included in media/ with a content-hash…
A long-context retrieval and question-answering benchmark with 1,143,371 documents and 10,000 questions, including 50 questions about 0G. It extends the ms100M bank from MSA-RAG-BENCHMARKS with 20 million additional text tokens and 655 new questions grounded in the added documents. 120M is the benchmark's nominal size. The actual corpus contains 125,708,601 raw tokens under the MSA-4B tokenizer: 105,708,601 original tokens plus exactly 20,000,000 added tokens. Counts exclude prompts, wrappers, and special tokens. This is a derived benchmark, not an official Microsoft MS MARCO release. The original documents occupy positions 0:961686; the original questions occupy positions 0:9345. Their…
Model Collections
Hand-picked starting points, each with the reason it exists.
Collection · 4 entries
Embedding models for retrieval
Sentence and document embedding models used to build retrieval systems. Dimension and sequence length matter more than size here, and both come from the publisher.
Collection · 4 entries
Models that fit on one accelerator
Models whose publisher-reported parameter count puts them within reach of a single accelerator at common precisions. Memory needed depends on precision and serving configuration, so treat the parameter count as the starting point, not the answer.
Collection · 6 entries
Open-weight text models worth knowing
Widely used open-weight language models, chosen because each one is a distinct family rather than a variant of the one above it. Selection, not a ranking.
Collection · 3 entries
Speech and audio models
Recognition and synthesis models, grouped so the two directions are easy to compare.
Related SAVRN Research
The hub sits beside SAVRN's market data and infrastructure research: what models cost to run, and what it takes to run them.
SAVRN Index
What open models cost to run
The same open-weight model priced by every host that serves it, per million tokens.
Research Hub
Data center trackers and maps
Moratoriums, permits, power, water and capital behind the facilities that run these models.
Method
How the Model Hub is built
Sources, evidence labels, refresh behaviour, and the limits of every comparison here.