datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
pascal-voc-2012pascal-voc
Pascal VOC
Dataset Summary
The Pascal Visual Object Classes (VOC) dataset is a widely used benchmark in the field of computer vision. It is designed for object detection, image classification, semantic segmentation, and action classification tasks. The dataset provides a comprehensive set of annotated images covering 20 object classes, allowing researchers to evaluate and compare the performance of various algorithms.
Note: This dataset repository contains all editions of… See the full description on the dataset page: https://huggingface.co/datasets/merve/pascal-voc.pascal_vocPASCAL_VOCPascal_VOCpascal-voc-2012-segmentation-lance
Pascal VOC 2012 Segmentation (Lance Format)
A Lance-formatted version of the Pascal VOC 2012 semantic segmentation split, sourced from nateraw/pascal-voc-2012. Each row pairs an inline JPEG image with the per-pixel PNG segmentation mask and a cosine-normalized OpenCLIP ViT-B-32 image embedding, so a single columnar table carries both annotation modalities and the features needed to retrieve, curate, and train against them — all available directly from the Hub at… See the full description on the dataset page: https://huggingface.co/datasets/lance-format/pascal-voc-2012-segmentation-lance.pascal-voc-2012
Dataset Card for "pascal-voc-2012"
More Information needed
pascal_voc2012_det_train_vallrgb-pascalvoc-neuralwalkerimport torch
from datasets import load_dataset
from torch_geometric.data import Data, Batch
from tqdm import tqdm
# Load the HuggingFace dataset from hub
hf_dataset = load_dataset("samm393/lrgb-pascalvoc-neuralwalker")
# Load multiple graphs
num_graphs = 10
# Convert each to PyG Data object
pyg_graphs = []
for i in tqdm(range(num_graphs), desc="Loading graphs"):
hf_row = hf_dataset['train'][i]
pyg_dict = {k: torch.tensor(v) if isinstance(v, list) else v for k, v in hf_row.items()}… See the full description on the dataset page: https://huggingface.co/datasets/ReasonGNN/lrgb-pascalvoc-neuralwalker.pascal-voc
Pascal VOC
Dataset Summary
The Pascal Visual Object Classes (VOC) dataset is a widely used benchmark in the field of computer vision. It is designed for object detection, image classification, semantic segmentation, and action classification tasks. The dataset provides a comprehensive set of annotated images covering 20 object classes, allowing researchers to evaluate and compare the performance of various algorithms.
Note: This dataset repository contains all editions of… See the full description on the dataset page: https://huggingface.co/datasets/yizhangdev/pascal-voc.pascal_voc_seg_train_valPASCAL_VOC_backup_from_JimmyUnleashedlrgb-pascalvoc-neuralwalker-walk-length-6import torch
from datasets import load_dataset
from torch_geometric.data import Data, Batch
from tqdm import tqdm
# Load the HuggingFace dataset from hub
hf_dataset = load_dataset("samm393/lrgb-pascalvoc-neuralwalker")
# Load multiple graphs
num_graphs = 10
# Convert each to PyG Data object
pyg_graphs = []
for i in tqdm(range(num_graphs), desc="Loading graphs"):
hf_row = hf_dataset['train'][i]
pyg_dict = {k: torch.tensor(v) if isinstance(v, list) else v for k, v in hf_row.items()}… See the full description on the dataset page: https://huggingface.co/datasets/ReasonGNN/lrgb-pascalvoc-neuralwalker-walk-length-6.pascal_vocPascal_vocpascal_voc_2012pascal-voc-2012PascalVOC_NAMLab_pt
PASCAL VOC 2012 NAMLab Priors for HGA
This dataset repository hosts the pre-computed hierarchical invariant region partition priors for weakly supervised semantic segmentation (WSSS), generated using the NAMLab framework.
These priors serve as the 2D structural guidance stream for the HGA (Hierarchical-Geometric Alignment) boundary internalization paradigm.
🔗 Main Codebase
The complete implementation, environment setup, and training guidelines are available in… See the full description on the dataset page: https://huggingface.co/datasets/Uncertainty-42/PascalVOC_NAMLab_pt.