Cosine_metric_learning

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cosine_metric_learning is an Open Source & Models tool that provides code for training a metric feature representation for person re-identification. It is designed for use with the deep_sort tracker.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is cosine_metric_learning?

cosine_metric_learning offers a repository with code for training a metric feature representation, specifically tailored for person re-identification tasks. This tool is intended to be used in conjunction with the deep_sort tracker, implementing the approach described in the 'Deep Cosine Metric Learning for Person Re-identification' paper. It includes functionalities to train models on datasets like Market1501 and MARS, with options for different loss modes such as cosine-softmax. Users can monitor training progress and evaluation metrics using TensorBoard, export features for testing, and freeze trained models for deployment with Deep SORT. The repository provides detailed instructions for setting up datasets, initiating training, and evaluating model performance.

Best used for

Ideal for developers and data scientists who need to train deep learning models for person re-identification, evaluate their performance on standard datasets like Market1501 and MARS, and integrate these models with the deep_sort tracker. Especially valuable for researchers and practitioners working on computer vision applications requiring robust identity tracking.

Common actions

train deep learning models

perform person re-identification

integrate with object trackers

evaluate model performance

collaborationlow-code/no-codeautomated workflowworkflowsdeepfakeopen-sourceface swappinggithub copilot"AI Agents"

Capabilities

Key features

Metric feature representation training
Person re-identification
Deep SORT integration
Market1501 dataset support
MARS dataset support
TensorBoard monitoring
Model export

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What datasets does cosine_metric_learning support for training?

The cosine_metric_learning repository provides code and instructions for training models on both the Market1501 and MARS datasets. Users need to download these datasets separately and configure the training scripts accordingly to begin model development and evaluation.

How can I monitor the training progress and evaluation metrics?

Training progress and evaluation metrics can be monitored using TensorBoard. The repository generates TensorFlow checkpoints and evaluation logs in specified directories, which can then be visualized by running TensorBoard commands with the appropriate log directories and ports.

Can I export trained models for use with Deep SORT?

Yes, the repository includes functionality to export trained models into a '.pb' file format. This frozen model file can then be supplied directly to the Deep SORT tracker, enabling the integration of the learned metric features into a real-time tracking system.

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AI Agents & Automation › AI Frameworks & Infra Research & Education › Scientific Computing

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