prithivMLmods/OpenDetection-30K-Human-Preferences

[!IMPORTANT] fully cleaned and unified version: prithivMLmods/OpenDetection-80K-Unified-Cleaned

OpenDetection-30K-Human-Preferences

OpenDetection-30K-Human-Preferences is an object detection dataset built primarily from general human-preference, publicly available images, which make up the majority of the input imagery, together with additional publicly available datasets. The dataset contains object detection annotations generated using automated computer vision pipelines and is intended for research, benchmarking, and training object detection models. Each sample includes the original image, structured object annotations, and a rendered visualization showing the detected objects. This is the uncleaned version of the dataset and may contain samples with empty or null object annotations. It is provided to support research scenarios where the original annotation outputs are required without additional filtering or post-processing.

Dataset Statistics

Important Note

This dataset is the original uncleaned version.

Some samples may contain:

• —Empty object annotations (objects == [])
• —Null or missing detection results
• —Images without detected objects

These samples have been intentionally retained to preserve the original outputs generated by the annotation pipeline.

Dataset Structure

Each sample contains the following fields:

Example:

sample = ds[0]

print(sample.keys())

# dict_keys([
#     "image",
#     "objects",
#     "annotated_image"
# ])

Loading the Dataset

from datasets import load_dataset

ds = load_dataset(
    "prithivMLmods/OpenDetection-30K-Human-Preferences",
    split="train"
)

Example Usage

from datasets import load_dataset
import matplotlib.pyplot as plt

ds = load_dataset(
    "prithivMLmods/OpenDetection-30K-Human-Preferences",
    split="train"
)

sample = ds[0]

image = sample["image"]
objects = sample["objects"]
annotated = sample["annotated_image"]

print("Detected Objects:")
print(objects)

fig, axes = plt.subplots(1, 2, figsize=(12, 6))

axes[0].imshow(image)
axes[0].set_title("Image")
axes[0].axis("off")

axes[1].imshow(annotated)
axes[1].set_title("Annotated Image")
axes[1].axis("off")

plt.show()

Dataset Features

• —30K human-preference image samples
• —Object detection annotations generated using automated pipelines
• —Original unfiltered annotation outputs
• —May contain empty or null object annotations
• —Bounding box visualizations for every sample
• —Optimized Parquet format for efficient loading
• —Compatible with the Hugging Face Datasets library
• —Suitable for object detection research, benchmarking, annotation analysis, and multimodal computer vision

License

This dataset is released under the Apache-2.0 License.