MeowID-Base

<p align="center"> <img src="assets/logo.png" alt="MeowID" width="420"> </p> <h1 align="center">MeowID: A Dual-Expert Retrieval System for Individual Cat Identification</h1> <p align="center"> <img alt="Version" src="https://img.shields.io/badge/version-0.3.0-11bfae"> <img alt="Embedding" src="https://img.shields.io/badge/embedding-512D-0875c1"> <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-supported-ee4c2c"> <img alt="ONNX" src="https://img.shields.io/badge/ONNX-supported-005ced"> <img alt="TensorRT" src="https://img.shields.io/badge/TensorRT-supported-032b4a"> </p> <p align="center"> <strong>TL;DR:</strong> MeowID prioritizes fine-grained facial evidence, augments it with whole-cat context, and falls back to whole-cat retrieval when a usable face is unavailable. </p> <div align="center"> Zhangchi Hu^1,2,*,†, Yi Shang^2,*, Haocheng Yang^4,2,*, Qiwei Hu^5,*, and Yuzheng Li^3,* </div> <p></p> <div align="center">_{¹ Department of Electronic Engineering and Information Science, University of Science and Technology of China<br> ² School of Intelligent Software Engineering, Hefei University of Technology<br> ³ School of Software Engineering, Sun Yat-sen University<br> ⁴ School of Computer Science, Northwestern Polytechnical University<br> ⁵ College of Biological Sciences and Technology, Beijing Forestry University}</div> <p align="center"> ^* Equal contribution &nbsp;&nbsp; ^† Project leader </p>

Model overview

MeowID is a face-priority, dual-expert retrieval system for identifying individual cats in unconstrained photographs. It combines separately parameterized face and whole-cat encoders while keeping their embedding galleries route-specific.

• When a usable aligned face is available, the face expert produces the primary representation and receives a gated whole-cat correction.
• When facial evidence is unavailable, the system falls back to the whole-cat expert.
• New identities can be enrolled through embedding extraction and gallery insertion without retraining the recognition models.
• All retrieval embeddings are L2-normalized, 512-dimensional vectors.

Method pipeline

<p align="center"> <img src="assets/meowid-pipeline.png" alt="MeowID method pipeline" width="100%"> </p>

The whole-cat expert produces an embedding for every image. A valid ECPose detection activates PetFace-style face alignment, the face expert, and validation-guided whole-cat hint fusion. Queries are compared only with the gallery associated with their selected route.

Repository contents

The TensorRT engines were built for the reference RTX 3090 environment. Rebuild them from the ONNX artifacts when the GPU architecture, TensorRT version, or batch profile changes.

Inference capabilities

Minimal Python usage

from cat_recognition import MeowID

model = MeowID(
    "artifacts/MeowID-Base",
    backend="tensorrt",
    device="cuda:0",
    registry="registries/demo",
)

model.register(
    "cat_001",
    ["images/cat_001_a.jpg", "images/cat_001_b.jpg"],
)

prediction = model.search("images/query.jpg", top_k=5)[0]
print("route:", prediction.embedding.route)
for match in prediction.matches:
    print(match.cat_id, match.score)

The package accepts file paths, directories, glob patterns, PIL images, RGB NumPy arrays, and iterables of supported inputs.

Reference results

Offline retrieval on the ICW test set:

End-to-end batch-1 measurements on one RTX 3090 over 2,846 ICW test images include image decoding, preprocessing, ECPose, alignment, embedding extraction, and routing:

These results describe the reference evaluation environment and do not guarantee production performance.

Model mirrors

• Hugging Face — RicePasteM/MeowID-Base
• ModelScope — RicePasteM/MeowID-Base

Citation

@misc{hu2026meowid,
  title  = {MeowID: A Dual-Expert Retrieval System for Individual Cat Identification},
  author = {Zhangchi Hu and Yi Shang and Haocheng Yang and Qiwei Hu and Yuzheng Li},
  year   = {2026}
}