prithivMLmods/OpenDetection-50K-Remastered-Cleaned

OpenDetection-50K-Remastered-Cleaned

OpenDetection-50K-Remastered-Cleaned is the cleaned version of OpenDetection-50K-Remastered, created by removing every sample that contains no detected objects. This ensures that every image in the dataset includes at least one valid object annotation, making the dataset more suitable for training, evaluation, and benchmarking object detection models. The dataset is built primarily from general, publicly available images, which make up the majority of the input imagery, together with additional publicly available datasets. Each sample contains the original image, structured object detection annotations including class labels, confidence scores, and bounding boxes, along with a rendered visualization showing all detected objects. The dataset is distributed using the Hugging Face Datasets format with optimized Parquet files for efficient loading and large-scale training workflows.

52,557 rows (81 rows with null or empty objects were removed) from prithivMLmods/OpenDetection-50K-Remastered.

Dataset Statistics

Property	Value
Number of Samples	52,557
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, confidence scores, label IDs, 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-50K-Remastered-Cleaned",
    split="train"
)

Example Usage

from datasets import load_dataset
import matplotlib.pyplot as plt

ds = load_dataset(
    "prithivMLmods/OpenDetection-50K-Remastered-Cleaned",
    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

• Cleaned version of OpenDetection-50K-Remastered
• All empty annotations removed
• Every image contains at least one valid object
• High-quality object detection annotations
• Bounding box visualizations for every sample
• Optimized Parquet format for efficient loading
• Compatible with the Hugging Face Datasets library
• Suitable for training, evaluation, benchmarking, and multimodal computer vision research

License

This dataset is released under the Apache-2.0 License.