Datasets:
dataset_info:
features:
- name: image
dtype: image
- name: objects
dtype: string
- name: annotated_image
dtype: image
splits:
- name: train
num_bytes: 11671106327
num_examples: 30000
download_size: 13858153622
dataset_size: 11671106327
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- unified
- computer-vision
- object-detection
- vision
- image
- bounding-boxes
- multimodal
- detection
- machine-learning
- deep-learning
- coco-format
- open-vocabulary-detection
- auto-annotation
- vlm
license: apache-2.0
task_categories:
- object-detection
language:
- en
pretty_name: OpenDetection
size_categories:
- 10K<n<100K
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
| Property | Value |
|---|---|
| Number of Samples | 30,000 |
| Image Format | RGB |
| Annotation Format | JSON |
| Visualization | Annotated Image |
| Dataset Format | Optimized Parquet |
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:
| 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-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.