DamianBoborzi/objaverse_processed_renders_and_captions

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