Detection Annotations — Pascal VOC (XML)
The same detection boxes in the Pascal VOC XML format — a per-image annotation with size, and one object element per box with pixel corner coordinates. The XML twin of the COCO and YOLO annotations.
<annotation>
<folder>vision</folder>
<filename>street-scene.png</filename>
<source>
<database>Novus Examples</database>
</source>
<size>
<width>640</width>
<height>480</height>
<depth>3</depth>
</size>
<segmented>0</segmented>
<object>
<name>person</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>90</xmin>
<ymin>210</ymin>
<xmax>160</xmax>
<ymax>390</ymax>
</bndbox>
</object>
<object>
<name>car</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>300</xmin>
<ymin>300</ymin>
<xmax>540</xmax>
<ymax>420</ymax>
</bndbox>
</object>
<object>
<name>tree</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>500</xmin>
<ymin>90</ymin>
<xmax>610</xmax>
<ymax>410</ymax>
</bndbox>
</object>
</annotation>
Specifications
- Format
- Pascal VOC
- Objects
- 3
- Bbox
- xmin, ymin, xmax, ymax
What is a .xml file?
XML (Extensible Markup Language) is a verbose, self-describing markup language using nested tags, attributes, and namespaces to represent structured, hierarchical data. It supports schemas, entities, and validation and underlies many document and data formats. It remains common in enterprise, publishing, and interchange contexts.
How to use this file
Use an example XML file to test parsers, namespace and schema validation, XPath queries, and protection against entity-expansion and external-entity attacks.
Code examples
import xml.etree.ElementTree as ET
tree = ET.parse("annotations.voc.xml")
root = tree.getroot()
print(root.tag, [c.tag for c in root][:5])Related files
- pngObject-detection Scene (PNG, 640×480)A simple rendered street scene with a person, a car, and a tree at known pixel coordinates — the image the COCO, YOLO, and Pascal-VOC annotation twins describe. A fixture for testing object-detection loaders and annotation converters.
- txtDetection Class List (TXT)The class-name list for the detection scene, one label per line — index equals the zero-based line number, matching the YOLO class ids. A companion to the COCO/YOLO/VOC annotation files.
- jsonText Embeddings — 16-dim (JSON)A set of 24 L2-normalised 16-dimensional text embeddings as JSON — each record pairs an id and its source text with a float vector. A fixture for testing vector stores, similarity search, and embedding loaders. Parquet and .npy twins included.
- parquetText Embeddings — 16-dim (Parquet)The same 16-dimensional embeddings as Apache Parquet — id and text columns plus one column per dimension. The columnar twin, for testing analytics engines and Parquet-based vector pipelines.
- npyText Embeddings — 24×16 matrix (NumPy .npy)The embeddings as a raw NumPy array — a 24×16 float32 matrix in .npy format, loadable with numpy.load. The binary twin of the JSON and Parquet files, for testing tensor and matrix loaders.
- safetensorsTiny Model Weights (safetensors)A genuinely-valid safetensors file with two small float32 tensors (36 parameters total) — an 8×4 weight and a length-4 bias. The values are meaningless sample data, not a trained model; a fixture for testing safetensors loaders and weight inspectors.
Generated by generation/ai_datasets.py. Free for any use, no attribution required — license.