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umair894/
KAT-ReID-Veri776

Name: KAT-ReID-Veri776
Availability: InStock

apache-2.0

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KAT-ReID – VeRi-776

Model Details

Model name: KAT-ReID (VeRi-776)
Architecture: Kolmogorov–Arnold Transformer (KAT) with GR-KAN channel mixers
Task: Vehicle Re-Identification
Dataset: VeRi-776
Framework: PyTorch
License: MIT
Paper: KAT-ReID: Assessing the Viability of Kolmogorov–Arnold Transformers in Object Re-Identification

This model replaces the MLP blocks of a ViT-based ReID backbone with Group-Rational Kolmogorov–Arnold Networks (GR-KAN) while retaining self-attention and ReID-specific architectural components.

Model Description

The model is trained for vehicle re-identification, where the goal is to retrieve images of the same vehicle across different cameras and viewpoints.

Key architectural features:

GR-KAN replaces standard MLP channel mixers
Side-information embedding (camera/view conditioning)
Local token rearrangement branch to preserve spatial cues
Joint optimization with ID classification and metric learning losses

Training Data

Dataset: VeRi-776
Identities: 776 vehicles
Images: 49,357
Cameras: 20
Views: 8

Training follows the official dataset split and evaluation protocol.

Training Procedure

Pretraining: ImageNet-1K
Input resolution: 256 × 128
Patch size: 16 × 16 (overlapping stride 12)
Optimizer: SGD (lr=0.008, momentum=0.9)
Batch size: 64 (16 IDs × 4 images)
Losses: Cross-Entropy (ID) + Triplet loss
Augmentations: Random flip, random erasing
Mixed precision: Enabled

Evaluation Results

Metric	Score
mAP	59.5
Rank-1	88.0
Rank-5	95.8
Rank-10	98.0

Results are reported under single-query evaluation without re-ranking.

Intended Use

This model is intended for:

Academic research in ReID
Benchmarking alternative transformer channel mixers
Studying robustness under viewpoint variation

Not intended for real-world surveillance or deployment without further validation.

Limitations

Underperforms strong ViT baselines on globally discriminative vehicle benchmarks
Sensitive to training stability due to rational activation parameterization
Performance may vary with different camera distributions

Citation

bibtex

@inproceedings{umair2025katreid,
  title={KAT-ReID: Assessing the Viability of Kolmogorov--Arnold Transformers in Object Re-Identification},
  author={Umair, Muhammad and Zhou, Jun and Musaddiq, Muhammad Hammad and Muhammad, Ahmad},
  year={2025}
}

umair894/KAT-ReID-Veri776