Mdistiller

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mdistiller is an Open Source AI library that provides classical knowledge distillation algorithms for computer vision benchmarks. It includes official implementations of Decoupled Knowledge Distillation and DOT: A Distillation-Oriented Trainer.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is mdistiller?

mdistiller is a comprehensive PyTorch library designed for implementing and experimenting with classical knowledge distillation algorithms across mainstream computer vision benchmarks. It serves as the official implementation for two significant research papers: "Decoupled Knowledge Distillation" (CVPR 2022) and "DOT: A Distillation-Oriented Trainer" (ICCV 2023). The library supports various distillation methods on datasets like CIFAR-100, ImageNet, and MS-COCO, offering researchers and developers a robust framework for model compression and performance enhancement. It provides tools for evaluation, training, and extending custom distillation methods, making it a valuable resource for advanced AI research and development.

Best used for

Ideal for developers and data scientists who need to apply knowledge distillation techniques to deep learning models, evaluate model performance on standard benchmarks, and extend existing distillation methods. Especially valuable for researchers working on model compression and efficiency in computer vision.

Common actions

implement knowledge distillation

evaluate AI models

train deep learning models

research model compression

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Capabilities

Key features

Classical knowledge distillation algorithms
Decoupled Knowledge Distillation (DKD)
Distillation-Oriented Trainer (DOT)
Supports CIFAR-100, ImageNet, MS-COCO
Custom distillation method extension
PyTorch library

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the primary research papers implemented by mdistiller?

mdistiller officially implements the CVPR 2022 paper "Decoupled Knowledge Distillation" and the ICCV 2023 paper "DOT: A Distillation-Oriented Trainer." These papers introduce advanced techniques for improving knowledge transfer in deep learning models.

Which computer vision benchmarks does mdistiller support?

The library supports various knowledge distillation methods on mainstream computer vision benchmarks including CIFAR-100, ImageNet, and MS-COCO. This allows for broad applicability and evaluation across different types of image datasets.

Can I extend mdistiller with my own custom distillation methods?

Yes, mdistiller is designed to be extensible. Users can create custom Python files to define new distillers, register them within the library, and configure corresponding hyperparameters, enabling flexible research and development.

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