Update README.md

README.md

@@ -39,4 +39,103 @@ language:
 pretty_name: OpenDetection
 size_categories:
 - 10K<n<100K
----
+---
+
+# **OpenDetection-15K-Dense-v1.0**
+
+**OpenDetection-15K-Dense-v1.0** 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. The dataset is intended for training, evaluation, benchmarking, and multimodal computer vision research, and 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:
+
+```python
+sample = ds[0]
+
+print(sample.keys())
+
+# dict_keys([
+#     "image",
+#     "objects",
+#     "annotated_image"
+# ])
+```
+
+## Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset(
+    "prithivMLmods/OpenDetection-15K-Dense-v1.0",
+    split="train"
+)
+```
+
+## Example Usage
+
+```python
+from datasets import load_dataset
+import matplotlib.pyplot as plt
+
+ds = load_dataset(
+    "prithivMLmods/OpenDetection-15K-Dense-v1.0",
+    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**.