prithivMLmods/OpenDetection-15K-Dense-v2.0

OpenDetection-15K-Dense-v2.0

OpenDetection-15K-Dense-v2.0 is an object detection dataset built primarily from general, publicly available images, which make up the majority of the input imagery, together with additional publicly available datasets. The dataset contains high-quality object detection annotations generated using modern automated computer vision pipelines, providing structured object labels, confidence scores, and bounding box coordinates for every detected object. Each sample includes the original image, machine-readable detection annotations, and a rendered visualization with all detected objects overlaid on the image. Version 2.0 contains a different curated subset of images while maintaining the same annotation format and dataset structure, making it suitable for training, evaluation, benchmarking, and multimodal computer vision research. The dataset is distributed using the Hugging Face Datasets format with optimized Parquet files for efficient loading and large-scale machine learning workflows.

Dataset Statistics

Property	Value
Number of Samples	14,911
Image Format	RGB
Annotation Format	JSON
Visualization	Annotated Image
Dataset Format	Optimized Parquet

Dataset Structure

Each sample contains the following fields:

Column	Type	Description
image	Image	Original input image
objects	List	Object detection annotations containing labels, label IDs, confidence scores, and bounding boxes
annotated_image	Image	Visualization of the image with rendered bounding boxes

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-15K-Dense-v2.0",
    split="train"
)

Example Usage

from datasets import load_dataset
import matplotlib.pyplot as plt

ds = load_dataset(
    "prithivMLmods/OpenDetection-15K-Dense-v2.0",
    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

• High-quality object detection annotations
• Structured JSON object annotations
• Bounding box coordinates for every detected object
• Confidence scores for each detection
• Annotated visualization for every image
• Optimized Parquet format for efficient loading
• Compatible with the Hugging Face Datasets library
• Suitable for object detection training, evaluation, benchmarking, and multimodal computer vision research

License

This dataset is released under the Apache-2.0 License.