domain-reid

Guide person re-identification workflows with BNNeck trick and loss configuration.

5|2|Updated Feb 26, 2026

Search Tags:#machine learning #person re-identification #re-id #data sets #ML gotchas

Ready to run

npx skills add https://github.com/AxGord/claude-workflow --skill domain-reid

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: domain-reid
Source: https://github.com/AxGord/claude-workflow/tree/main/templates/skills/domain-reid
Command: npx skills add https://github.com/AxGord/claude-workflow --skill domain-reid

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill unit provides critical insights and gotchas in person re-identification, ensuring that machine learning workflows in this domain are optimized and avoid common pitfalls.

Core Features & Use Cases

BNNeck Distance Metric Direction: Offers clarity on training and inference distances in triplet loss, correcting common errors in feature processing.
Loss Recipe Guidance: Explains the balance between cross-entropy, triplet mining, and center loss in loss calculation, critical for stability.
CLIP-ReID Best Practices: Describes the two-stage process of CLIP-ReID and highlights SOTA performance.
Performance References: Provides SOTA performance numbers across key datasets, essential for comparing methods.
Dataset Overview: Details the characteristics and uses of popular re-identification datasets, guiding data selection.

Quick Start

Review the 'BNNeck Trick' to understand distance metric direction for effective person re-identification.

Frequently Asked Questions about domain-reid

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I configure the BNNeck trick for person re-identification?▼

The BNNeck trick for person re-identification separates feature spaces by applying batch normalization before the identity classifier, allowing Euclidean distance for triplet loss training and cosine distance for inference.

What is the best way to balance loss functions in person re-identification?▼

The best way to balance loss in person re-identification is combining cross-entropy, triplet mining, and center loss. This mixture ensures training stability by simultaneously optimizing classification boundaries, inter-class distance, and intra-class feature compactness.

How does CLIP-ReID work for person re-identification?▼

CLIP-ReID works through a two-stage process for person re-identification. It first optimizes text prompts to learn visual-language prompts, then fine-tunes the image encoder using the learned prompts to achieve state-of-the-art performance.

Which datasets are used for benchmarking person re-identification performance?▼

Standard datasets for benchmarking person re-identification performance provide specific characteristics for evaluating models. Comparing your results against these state-of-the-art performance numbers helps validate that your workflow avoids common ML pitfalls.

Why does my triplet loss training fail on person re-identification tasks?▼

Triplet loss training fails on person re-identification tasks when distance metric directions are misapplied. Using the wrong distance metric during feature processing disrupts the separation of intra-class and inter-class feature distributions.