dense
Datasets
All datasets matching “dense”matrixcity_large_folder_denseCharge-040_0040-DenseTrainfa-task01-v14-train300-dense-raw-episodes-13c62ec5cf12
task01-v14-train300-dense-raw-episodes
Versioned fleet archive. Canonical recipe: datasets/task01-v14-train300-dense/collection-20260923
Tier: raw Codegen collection episodes, 300 instances / 305 attempts (success, failure and infrastructure-error attempts): per-attempt policy actions/events, evaluator logs, evaluation json and per-attempt LeRobot export
Use the exact recorded revision and verify SHA256SUMS. This package is a snapshot, not a live directory mirror.
DenseFusion-1M
[Paper] https://arxiv.org/abs/2407.08303
[GitHub] https://github.com/baaivision/DenseFusion
Introduction
An image is worth a thousand words". Comprehensive image descriptions are essential for multi-modal perception, while images contains various visual elements of different granularities that are challenging to harness.
We propose Perceptural Fusion to integrate the diverse visual perception experts for capturing visual elements and adopt a MLLM as a centric pivot for… See the full description on the dataset page: https://huggingface.co/datasets/BAAI/DenseFusion-1M.DensePose-COCO
Dataset Card for DensePose-COCO
DensePose-COCO is a large-scale ground-truth dataset with image-to-surface correspondences manually annotated on COCO images.
This is a FiftyOne dataset with 33929 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
import fiftyone.utils.huggingface as fouh
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset =… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/DensePose-COCO.dresscode_agnostic_and_densepose
DressCode Agnostic & DensePose Dataset
Agnostic images, corresponding masks, and DensePose images for the DressCode dataset.
Information about the usage can be found at:
https://github.com/jiwoohong93/ita-mdt_code
License
The Dress Code Dataset is proprietary to and © Yoox Net-a-Porter Group S.p.A. and its licensors.It is distributed by the University of Modena and Reggio Emilia and is available for non-commercial academic use under the licence terms provided… See the full description on the dataset page: https://huggingface.co/datasets/jiwoohong93/dresscode_agnostic_and_densepose.