datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
SparseVideoNav
SparseVideoNav Datasets
This repository contains the real-world navigation datasets released with OpenDriveLab/SparseVideoNav:
BVN: Beyond-the-View Navigation.
IFN: Instruction-Following Navigation.
Project links:
Project page: https://opendrivelab.com/SparseVideoNav
GitHub: https://github.com/OpenDriveLab/SparseVideoNav
Paper: https://arxiv.org/abs/2602.05827
Dataset Summary
SparseVideoNav studies real-world vision-language navigation with sparse future… See the full description on the dataset page: https://huggingface.co/datasets/OpenDriveLab/SparseVideoNav.SparseCraft-dataset
SparseCraft
[ECCV'24] SparseCraft: Few-Shot Neural Reconstruction through Stereopsis Guided Geometric Linearization
Project
DTU Dataset
We provide preprocessed DTU data and results for the tasks of novel view synthesis and surface reconstruction.
It contains the following directories:
sparsecraft_data
├── nvs # Novel View Synthesis task data and results
│ └── mvs_data
│ ├── scan103
│ ├── ...
│ └── results # Results for training using… See the full description on the dataset page: https://huggingface.co/datasets/maeyounes/SparseCraft-dataset.Dr.Sparse-RL-train-562
Dr.Sparse SpGEMM training pool (562 matrices)
The complete RL / selector training pool of Dr.Sparse (branch v2): 562 SuiteSparse matrices in the
harness .bin layout (int32 rows, cols, nnz; int32 row_ptr; int32 col_ind; float32 values; float32 x),
laid out as level1_small/ (91), level2_medium/ (273), level3_large/ (198); the huge tier is deliberately left out of training and evaluation.
Every matrix has a cuSPARSE SpGEMM reference (C = AA, or AA^T when rectangular) on an H200;… See the full description on the dataset page: https://huggingface.co/datasets/KinGeorge/Dr.Sparse-RL-train-562.stack-v2-sparse-classes-10k
Stack v2 Sparse Python Classes 10k
This is a 10,000-sample snapshot for Diffusion + Autoregressive hybrid code generation experiments.
Source
The data is extracted from bigcode/the-stack-v2-dedup, Python subset. The extraction uses Stack v2 metadata as source of truth, groups candidates by repo_name + revision_id, fetches files with git partial fetch + sparse checkout, then applies AST-level class filters.
Splits
train.jsonl: 9,000
val.jsonl: 500
test.jsonl:… See the full description on the dataset page: https://huggingface.co/datasets/hybrid-diff-ar/stack-v2-sparse-classes-10k.stack-v2-sparse-classes-75kplus
Stack v2 Sparse Python Classes 75kplus
This is a frozen snapshot with 75829 samples for Diffusion + Autoregressive hybrid code generation experiments.
Splits
train.jsonl: 74829
val.jsonl: 500
test.jsonl: 500
all.jsonl: 75829
Source
The data is extracted from bigcode/the-stack-v2-dedup, Python subset. The extraction uses Stack v2 metadata as source of truth, groups candidates by repo_name + revision_id, fetches files with git partial fetch + sparse checkout… See the full description on the dataset page: https://huggingface.co/datasets/hybrid-diff-ar/stack-v2-sparse-classes-75kplus.stack-v2-sparse-classes-36k
Stack v2 Sparse Python Classes 36k
This is a 36,000-sample snapshot for Diffusion + Autoregressive hybrid code generation experiments.
Source
The data is extracted from bigcode/the-stack-v2-dedup, Python subset. The extraction uses Stack v2 metadata as source of truth, groups candidates by repo_name + revision_id, fetches files with git partial fetch + sparse checkout, then applies AST-level class filters.
Splits
train.jsonl: 35,000
val.jsonl: 500
test.jsonl:… See the full description on the dataset page: https://huggingface.co/datasets/hybrid-diff-ar/stack-v2-sparse-classes-36k.