Datasets:
Dataset Viewer (First 5GB)
json
dict | numpy_0.npy
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| numpy_1.npy
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| numpy_2.npy
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| numpy_3.npy
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| render_0.png
imagewidth (px) 440
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| render_1.png
imagewidth (px) 440
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| render_2.png
imagewidth (px) 440
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| render_3.png
imagewidth (px) 440
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| __key__
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"caption": "Three-dimensional geometric composition consisting of elongated cuboids in vivid magenta against a neutral background. \n",
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{
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"caption": "Monochrome a cloud-like shape with a fluffy texture, accompanied by a dangling spherical object connected by a tapered cylindrical link. \n[\"Santa Claus hat with a cylindrical plush white rim, a soft and drooping conical red body, and a fluffy white pom-pom at the tip.\", \"Santa Claus hat, with cylindrical white rim, conical red body, fluffy white pom-pom at the tip.\", \"Santa Claus hat, cylindrical white plush rim, soft red conical body, fluffy white tassel.\", \"White cylindrical-rimmed Santa hat, fluffy pom-pom, drooping soft red body.\", \"Santa hat with plush white rim, red body, fluffy white tip.\", \"Santa hat, drooping red body, white fluffy rim and tip.\", \"Santa Claus hat with plush rim and pom-pom.\", \"Santa hat with white rim and pom-pom.\", \"Santa hat, white rim, fluffy pom-pom.\", \"Santa hat, plush rim, fluffy tip.\", \"Santa hat with pom-pom.\"] \n",
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"caption": "Classic muscle car, red with twin white racing stripes, black roof, chrome wheels, and stylized rear taillight panel. \n[\"A stylized classic muscle car with a long hood, short rear deck, two-door coupe body, bright red-orange with white racing stripes, round headlights, sleek sides without mirrors or handles, chromed rims, flat roof, minimalistic rear, no emblems.\", \"Bright red-orange classic muscle car with white stripes, long hood, and chromed rims\", \"Red-orange two-door coupe, long hood with white stripes, chrome rims\", \"Stylized muscle car, red-orange, with white stripes, chromed rims\", \"Classic car, red-orange with white stripes, long hood\", \"Muscle car with white stripes and chromed rims\", \"Red-orange two-door coupe, white stripes\", \"Classic muscle car with white stripes\", \"Red-orange car with chromed rims\", \"Muscle car, white stripes\", \"Red-orange car\"] \n",
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||||
{
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"caption": "The image shows a 3D model of a building with trees in front of it, set against a dark background. The building is animated, giving it a dynamic feel.; The image shows a low poly city with buildings, trees, and clouds in the sky. The buildings are brightly colored and the trees are lush and green. The clouds are white and fluffy, adding a peaceful atmosphere to the scene.; The image shows a 3D model of a building with a lot of windows, surrounded by trees and a road. The background is dark, giving the image an animated feel.; The image shows a low poly city with buildings, trees, and clouds in the sky. The buildings are brightly colored and the trees are lush and green. The clouds are white and fluffy, adding a peaceful atmosphere to the scene.",
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End of preview. Expand
in Data Studio
Contains rendered views and captions from Objaverse XL objects. the objects are from the alignment and TRELLIS500K (over 1 Millionen processed objects) dataset. We downloaded and rendered 4 views of each object. We added TRELLIS and CAP3D Captions where available. If there were no captions we generated new captions with the large version of Florence 2. This is the base dataset we used to generate MeshFleet which is described in MeshFleet: Filtered and Annotated 3D Vehicle Dataset for Domain Specific Generative Modeling. The rendered views and the captions from three different sources (CAP3D, TRELLIS500K and newly generated ones with Florence 2) are in the data directory split into webdataset chunks.
Dataset Information:
IterableDatasetDict({
train: IterableDataset({
features: ['json', 'numpy_0.npy', 'numpy_1.npy', 'numpy_2.npy', 'numpy_3.npy', 'render_0.png', 'render_1.png', 'render_2.png', 'render_3.png', '__key__', '__url__'],
num_shards: 206
})
})
Example usage:
from datasets import load_dataset
repo_id = "DamianBoborzi/objaverse_processed_renders_and_captions"
# Load the dataset with streaming enabled
hf_dataset = load_dataset(repo_id, streaming=True)
# Iterate over the dataset
split_name = list(hf_dataset.keys())[0]
dataset_split = hf_dataset[split_name]
print("Streaming the first 5 examples:")
count = 0
for example in dataset_split:
if count >= 5:
break
print(f"Example {count}: Keys = {list(example.keys())}")
# Process the example data here
count += 1
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